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  • Published: 14 December 2021

Research evolution in banking performance: a bibliometric analysis

  • S. M. Shamsul Alam 1 ,
  • Mohammad Abdul Matin Chowdhury   ORCID: orcid.org/0000-0001-6860-2305 1 &
  • Dzuljastri Bin Abdul Razak 1  

Future Business Journal volume  7 , Article number:  66 ( 2021 ) Cite this article

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Banking performance has been regarded as a crucial factor of economic growth. Banks collect deposits from surplus and provide loans to the investors that contribute to the total economic growth. Recent development in the banking industry is channelling the funds and participating in economic activities directly. Hence, academic researchers are gradually showing their concern on banking performance and its effect on economic growth. Therefore, this study aims to explore the academic researchers on this particular academic research article. By extracting data from the web of Science online database, this study employed the bibliometrix package (biblioshiny) in the ‘R’ and VOSviewer tool to conduct performance and science mapping analyses. A total of 1308 research documents were analysed, and 36 documents were critically reviewed. The findings exhibited a recent growth in academic publications. Three major themes are mainly identified, efficiency measurement, corporate governance effect and impact on economic growth. Besides, the content analysis represents the most common analysis techniques used in the past studies, namely DEA and GMM. The findings of this study will be beneficial to both bank managers and owners to gauge a better understanding of banking performance. Meanwhile, academic researchers and students may find the findings and suggestions to study in the banking area.

Introduction

The financial services formed a significant contributory trademark in the overall economic growth by stimulating employment, offering vast avenues for investment and services to the consumers and the society [ 1 ]. Thus, economic development is led by economic growth whereby required capital is provided by the financial services [ 2 ]. Suggestively, capital creation by the financial services industry through accumulation and mobilisation of resources is considered the most crucial economic growth strategy component [ 3 ]. The banking system associates with creating funds by accumulating funds from surplus and channelling them to the investors as credit; those exhibit excellent ideas to generate a surplus in the economy but lack the capital to implement such ideas [ 4 , 5 ]. Accordingly, the banking system plays a vital role to pledge the leading role of finance in economic development and promoting stable and healthy financial and economic development [ 6 ].

Banking performance has been regarded as a crucial factor of economic growth [ 7 ]. Efficiency and productivity change measures are rapidly used to evaluate banking performance. Academic researchers have been focusing on the efficiency and productivity of banking institutions for a long period, while economic growth is carried out in the discussions. Discovering research activities on banking efficiency and productivity in economic growth enables researchers to identify the local and international input to this particular discipline. More so, it will enable researchers to identify the ‘hot spots’ discussed by academic researchers and find the research gaps [ 8 ]. Indeed, banking performance in standings is a broad scientific topic, and estimating research activities might not be useful. For instance, research activities in this area extended to several constituents such as methodological approaches, banking approaches. In the current study, banking efficiency and productivity are considered as banking performance that contributes to the economic growth of an economy. Therefore, the main objective of this study is to explore the research activities of banking performance to economic growth. The investigation of banking performance research activities will enable the researchers to find the present directions of the research area and thus speculates the future research suggestions. Besides, it will also enable to expound the depth of past research activities and themes on banking performance relating to the economic growth measurements.

The use of the bibliometric method is appropriate to demonstrate the research shape and activity, volume and growth in a specific discipline [ 9 ]. A bibliometric method is a quantitative application of bibliometric data [ 10 ]. It analyses a wide-ranging quantity of published research articles employing the statistical tool to identify trends and citations or/and co-citations of a certain theme by year, author, country, journal, theory, method, and research constituent [ 11 ]. Significantly, this technique further distinguishes key research themes and active researchers, countries and institutions for future research planning and funding [ 12 ]. Scholars apply this method for several reasons: to reveal emerging trends in published research articles and journal performance, cooperation patterns, and research elements, and to reconnoitre the intellectual edifice of an exact domain in the existing literature [ 9 , 13 ].

Minimal studies have used bibliometric analysis related to banks. For instance, Violeta and Gordana have employed bibliometric analysis to spot the trends of DEA application in banking [ 14 ]. Another study conducted by Ikra et al. applied the bibliometric method to Islamic banking efficiency [ 15 ]. By an extensive search on the Scopus, Web of Science and Google Scholar, no such study was found related to bibliometric analysis on banking performance to the economic growth. Nevertheless, this study will be the first attempt to conduct bibliometric methods on the banking performance to the economic growth that could be the basis for future studies.

The findings of this study unfolded several contributions to both policymakers, bank managers and academic researchers. Firstly, the findings would benefit the policymakers regarding the contribution and trends of banking performance. It would allow them to take necessary initiatives to promote and improve banking performance, thus economic development. Meanwhile, bank managers may utilise the findings to strengthen their banking operations by acknowledging key factors that contributed to the performance. Finally, academic researchers are enabled to detect the current trend and topics related to the banking area that leads to further studies.

Bibliometric analysis has achieved enormous popularity in social sciences research in the current years [ 9 , 16 , 17 , 18 ]. The popularity of bibliometric analysis is observed from the development, accessibility and availability of software, for instance, Leximancer, Gephi, VOSviewer, Biblioshiny and publication databases (Web of Science and Scopus). Further, the rapid growth of bibliometric analysis in scientific production has emerged from business research to information science [ 9 ]. The popularity of bibliometric methodology in social science research is not a trend but moderately an image of its usefulness for constructing high research impact by handling excessive scientific data [ 9 ].

The bibliometric analysis is beneficial for briefing the trends in the research documents classifying ‘blind spots’ and ‘hot spots’, and finding a more inclusive understanding of the published research documents [ 19 ]. In detail, this analysis empowers the recognition of the most advanced (hot spots) and the less established topics (blind spots) within the documents that, shared with other bibliometric procedures, recommend future research avenues. The bibliometric analysis uncovers several ascriptions, such as unveiling emerging trends in documents and the performance of journals, research constituents and collaboration patterns and discovering the intellectual edifice of an exact domain in the existing literature [ 13 , 18 ]. The data that apply in this analysis incline to be immense (hundreds, thousands) and unbiased in nature (publications and citations number, keywords occurrences and topics). However, its explanations often depend on both subjective (thematic analysis) and objective (performance analysis) assessments formed through well-versed techniques and procedures [ 9 ]. Therefore, this study applied bibliometric analysis to examine the general perspective on banking performance and economic growth.

Two categories are mainly manifest in the bibliometric techniques, namely, performance and science mapping. Precisely, research elements’ contributions are accounted for in the performance analysis, while the connections between research elements are focused on science mapping [ 9 ]. This study follows performance analysis, science mapping and network analysis suggested by Donthu et al. [ 9 ].

Data extraction process

Two primary databases, the Web of Science and the Scopus, are commonly used in the bibliometric analysis [ 20 ]. Both databases are prominent for the peer-reviewed published research articles. The data for this analysis were a collection of bibliographic data from the Web of Science. The Web of Science (WoS) is a multidisciplinary online database providing access to several citation databases, namely Science Citation Index Expanded (SCIE), Social Sciences Citation Index (SSCI), Emerging Sources Citation Index (ESCI), Arts and Humanities Citation Index (AHCI), Conference Proceedings Citation Index, Index Chemicus and Current Chemical Reactions [ 18 , 21 ].

This study has applied a two-stage data extraction process, following Bretas and Alon, Alon et al. and Apriliyanti and Alon [ 16 , 22 , 23 ] as shown in Fig.  1 . The choice of the keywords is crucial to ensure that it covers the total body of published documents on banking performance and economic growth [ 21 ]. Accordingly, the selection of keywords was carried out by reviewing several abstracts and authors’ keywords in most related literature on the Web of Science. The selected keywords were executed in the WoS online database on 9 August 2021. A combination of keyword search terms was considered; (1) ‘banking performance*’ to nail all discrepancies of the term such as the role of the bank, bank efficiency, bank productivity, banking efficiency, banking productivity, banking performance, bank performance, upon refining the search by including only research articles from the categories; economics, business finance, business, management, operations research management, social sciences mathematical measures and documents written in English.

figure 1

The second stage extracted raw data from the online database combined, checked for duplicate documents and merged using ‘R’. Further, the documents were filtered in the ‘biblioshiny’ tool to omit book chapters and conference proceedings. After the extraction process for the bibliometric analysis, several impactful documents were selected based on local and global citations to conduct content analysis. The content analysis allowed the researcher to identify the leading research scopes and trends. Further, it allows identifying the streams and recommendations for future studies [ 22 ]. A total of 36 documents were selected to conduct a comprehensive review and valuation of the documents.

Performance analysis

Performance analysis investigates the contributions of academic research elements to a particular discipline [ 24 ]. This analysis is naturally descriptive, which is the hallmark of bibliometric analysis [ 9 ]. It is a standard method in reviews to exhibit the performance of various research elements such as authors, countries, institutions and sources similar to the profile or background of respondents generally presented in empirical studies, albeit more statistically [ 9 , 18 ]. Many measures exist in the performance analysis; hence, the most protuberant measurements are publications number and citations per research constituent or year. The publication is considered productivity, whereby citation measures influence an impact [ 9 ]. Besides, citation per document and h -index associate both publications and citations with evaluating research performance [ 18 ].

Table 1 presents the publication’s performance of banking performance. The results show a total number of 1308 documents published from 1972 to the present. Among 2275 contributed authors, a total of 202 authors were solely, and 2106 authors collaborated to the publications. A total of 31,458 citations received by published documents lead to an average of 629.16 citations per year, while 775 in h -index and 1023 in g -index. Hence, the banking efficiency field acknowledged productivity of research published by an average of 26.16 documents per year whereby nearly two authors (CI = 1.9) published one article, and standardised collaboration is 0.43 (between 0 and 1).

The annual production of scientific publications on banking efficiency is presented in Fig.  2 . The first research article related to banking performance was published by Fraser and Rose [ 25 ], who studied the effect of new bank appearance in the market on bank performance. The annual growth of publications on banking performance or banking efficiency is recorded to 12.39%. The publications are significantly increasing in recent periods, especially from 2016 to the present. However, the mandated growth in publications is observed between 2004 and 2015, while earlier periods (1972–2003) were quite sluggish. In these consequences, academic researchers have started to focus on banking performance or banking efficiency in the recent period. As a result, it can be concluded that banking performance and its sphere are shaping upwards through the research contributions.

figure 2

Annual Scientific production

Science mapping

Science mapping investigates the connections between research elements [ 26 ] that relates to the intellectual connections and structural networks within research constituents [ 9 ]. The science mapping includes citation analysis, bibliographic coupling, co-citation analysis, co-occurrence network, collaboration techniques. When combined with network analysis, these techniques are instrumental in exhibiting the research area’s bibliometric edifice and intellectual structure [ 27 ].

Citation analysis

The citation analysis is a fundamental approach for science mapping that runs on the assumption that citations reproduce intellectual contributions and impact the research horizons [ 28 ]. This analysis shows the impact of published documents by measuring the number of citations they received [ 9 ]. Accordingly, it enables the discovery of the most influential and informative documents in a research constituent. Thus, it allows gathering insights into that constituent’s intellectual dynamics [ 9 ]. Table 2 presents the top 20 impactful and influential documents in the field of banking performance or efficiency. The analysis has discovered that a total of 1112 documents (85%) out of 1308 documents received global citations. The global citations refer to the number of citations received in the overall Web of Science citations. However, 196 documents (about 15%) have not received any citation; meanwhile, 130 documents (about 10%) received only one citation. A document written by Berger An received the highest number (665) of citations which was published in 1997. The second most influential document was written by Seiford [51] received a total of 549 citations, followed by the document written by Back (2013) received 512 citations. In fact, a total of four documents written by Berger An rank in the top 20 impactful research articles in the field of banking performance or efficiency.

Factually, the majority of the documents without citations was published in a recent period. At the same time, the highly cited documents were published quite earlier. To detect the immediate influence of more recent documents is to apply the measurement of an average citation per year [ 29 ]. By evaluating the average citations per year, nine out of ten documents are also among the top 10 documents. Perpetually, Beck [45] holds the highest number of average citations per year (56.89), followed by Berger An (2013) ranked second position (51.44) and Beltratti A (2012) ranked the following position (48.40). Based on the citation analysis, it can be elucidated that Berger An is the most influential author in the banking efficiency research constituent.

Co-occurrence analysis

Co-occurrence analysis was projected by Callon et al. [ 30 ], considered as content analysis that is useful in plotting the strength of connotation within keywords in textual data. In other words, co-occurrence analysis is an approach that investigates the actual content of the document itself [ 9 ]. It maps the pertinent literature straight from the associations of keywords shared by research articles [ 24 , 27 , 31 , 32 ]. The co-occurrence analysis deduces words to appear recurrently in a cluster. It exhibits conceptual or semantic groups of various topics or sub-topics considered by research constituents [ 9 , 24 ]. Cobo and Herrera signified that spotted clusters could be applied with few objectives [ 24 ]. For instance, they can be applied to analyse their progression by gauging extension across successive subperiods and measuring the research area through performance analysis. Figure  3 displays the co-occurrence of keywords within the bank efficiency research constituent. As the focus of this research, bank performance represents the larger node associated with corporate governance, financial performance, financial crisis, nonperforming loans and others. In these scenarios, the red-coloured cluster depicts that these subtopics or variables are directly associated related to the bank performance theme due to repetitive co-occurrence of those words. Likewise, the green-coloured cluster represents a theme related to bank efficiency associated with performance and ownership. In the same cluster, the nonparametric data envelopment analysis is extensively used to measure commercial banks' technical and cost efficiency and productivity. Parametric stochastic frontier analysis is narrowly observed in efficiency measurements comparably. The green-coloured cluster depicts the determinants of bank profitability including other impactful variables such as risk, competition, corporate governance. This cluster applied panel data in order to examine performance, financial development as well as economic growth. Each of the cluster identifies the interacted themes used in the published documents using co-occurrence of keywords.

figure 3

Co-occurrence of keywords, Tool: VOSviewer. Note the nodes represent the keywords, and the edges between words present their occurrence of interactions. Each colour of nodes represents a cluster/theme. The size of the node presents a greater frequency of occurrence

Collaboration networks

Collaboration analysis explores the associations within researchers in a particular constituent. It is a formal way of intellectual association among researchers [ 33 , 34 ]. Therefore, it is crucial to understand how researchers associate among themselves [ 9 ]. In the presence of growing theoretical and methodological complexity in research, intellectual networking (collaboration) has become commonplace [ 33 ]. Indeed, collaboration or interaction among researchers enables improvements in academic research; for instance, greater interactions among diverse researchers allow richer insights and greater clarity [ 35 ]. Researchers who collaborate form a network named ‘invisible collages’ whose research can help improve undertakings in the study field [ 36 ]. Figure  4 presents the collaboration network of authors those co-authored academic articles in banking efficiency. Based on the collaboration network, Wanke P (Universidade Federal do Rio de Janeiro) was the most collaborated author who co-authored with four authors from different institutions in different countries. At the same time, Matousek, R (University Kent), Hasan, I (Rensselaer Polytechnic Institute) and Mamatzakis, E (University of Sussex), have also exhibited as greater collaborative researchers. In these consequences, authors from different institutions and from different parts of the world are collaborating to the banking performance/efficiency field.

figure 4

Source : VOSviewer. Note the nodes represent the authors, and the size represents the frequency of contribution, the colour presents a cluster or a particular group, and the link shows the link among authors that collaborated for research articles

Authors’ collaboration networks.

Bibliographic coupling

Co-authorship or collaborative networks within the authors and other crucial facets in the collaboration networks are the collaboration of author-affiliated countries and institutions [ 31 ]. Figure  5 exhibits the collaboration network within authors’ affiliated organisations. University Malaya and University Utara Malaysia, University Malaya and University Putra Malaysia, University Malaya and University Fed Rio de Janeiro all depict a strong collaboration network. In general, all the institutions display an embellishment among the institutions within the same region.

figure 5

Source : VOSviewer

Bibliographic coupling of author-affiliated institutions.

Similar to co-authors’ affiliated institutions, the collaboration of authors’ country presents a steady association among authors’ connections that allow exploring comparative and concurrent research works. Figure  6 represents the network of collaborative authors’ affiliation countries. These countries include South Africa and the USA, England and the USA, Australia and the USA, Malaysia and the USA, Germany and the USA, representing a high proportion of authors’ affiliated institutions are in the USA with this country performing as a hub of co-authorship publications from 1972 to 2021.

figure 6

Collaborative authors’ affiliated countries

This study discusses trending themes based on the bibliometric findings and reviews of highly cited and most recent documents (see Appendix 1 ). It also indicated the type of study, theories, methods and main findings to suggest comprehensive future studies.

Research directions

Between 1991 and 2010, studies related to banking performance have posited several antecedents to banking performance. Figure  7 displays the trend topics based on author keywords that appeared between 1972 and 2010. Studies in this period mainly focused on mergers and acquisitions, information technology and transition economies that emerged from universal banking deregulation and bank privatisation. The financial crisis during 2008–2009 drew the attention of scholars to evaluate the banking performance. Idiosyncratically, this phenomenon has been acknowledged by researchers from 2010 to 2015, focusing on the role of corporate governance in the performance of the banking industry, including compensation, risk management, determinants of stock returns, capital buffer, productivity. Idiosyncratically, a vast of studies were conducted on Chinese commercial banks and the effect on their economic growth.

figure 7

Source : Biblioshiny analysis. Note the frequency of terms selected 3 times for 1972–2010, 5 times for 2011–2015, 10 times for 2016–2021

Trend topics in different periods.

In the recent period (2016–2021), diverse factors posited in the studies that dominantly present a significant interest from banking scholars. While studies earlier mainly focusing on efficiency and its contributing factors, recent periods extended research directions to multiple constituents. For example, how banks diversified their services and the role of human capital efficiency to the banking performance [ 37 ]. Bose et al. employed the effect of green banking on the performance that underpins the inclusion of the environmental sustainability approach by the banking industry [ 38 ]. Meanwhile, Bhattacharyya et al. showed the effect of CSR expenditures and financial inclusion on the performance that define the social sustainability indicator of the banks [ 39 ]. Repeatedly, the role and structure of the board, categorisation of deposits and loans, risk exposures (business cycle), macroeconomic factors were also acknowledged in recent banking performance studies [ 40 , 41 , 42 , 43 ]. Idiosyncratically, scholars recently focus the components of sustainability of the banking industry from economic, environmental and social aspects [ 44 ]. Furthermore, the effect of banking and its stability on economic growth has been broadly carried out in the recent period. Moreover, the development of studies was taken into account, which implies the contribution to the economic growth of particular regions. Based on the earlier and recent studies, it is precisely observed the diversification of research constituents in relation to bank performance studies. Earlier studies (up to 2015) mainly measured banking performance or efficiency based on accounting measurements, while recent studies started to include market measurements principally based on stock returns performance. On the other hand, the rise of Islamic banking and finance influenced academic researchers to compare the business models [ 45 ], banking efficiencies [ 46 ] between conventional and Islamic banks, and efficiency for Islamic banks [ 5 ].

Based on the review of impactful documents published from 1990 to 2010, two particular objectives were identified: the effect of the board of directors or ownership on the bank performance [ 47 , 48 , 49 ] and measurement of efficiency, including cost and profit efficiency [ 50 , 51 , 52 ]. These constituents extended during 2011–2020 by the inclusion of risk-taking management [ 53 ], CEO incentives [ 54 ], contributing factors including capital, banking crises on banking performance [ 42 , 55 , 56 , 57 ]. Meanwhile, the Islamic banking system got crucial attention from academic researchers. Accordingly, several studies evaluated and compared efficiency between Islamic and conventional banks [ 45 , 58 , 59 ]. Nevertheless, the role of the banking industry in economic growth was included in the research constituents in the recent decade. For example, Xu, Santana and a few more scholars investigated the correlation between financial intermediation and economic growth [ 57 , 60 , 61 ]. In recent years, scholars extended the banking-related research constituents to diverse areas. The effect of human capital efficiency [ 37 ], green banking [ 38 ], CSR expenditures [ 39 ] and bank stability was included to measure banking performance. These extensions of research themes within banking performance studies posited a significant interest by academic researchers.

Apparently, almost all documents adopted the quantitative method in measuring banking performance research constituents. However, studies that measured banking efficiency mainly applied nonparametric analysis DEA [ 5 , 51 ], while SFA was adopted by limited studies [ 37 , 42 , 43 ]. On the other hand, regression analysis was predominantly applied to investigate banking performance from 1990 to 2010 [ 49 , 50 ]. In recent studies, academic researchers have vastly adopted GMM (generalised method of moments) to examine the contributing factors on banking performance [ 39 , 42 , 57 , 60 ]. These methods are dominating the banking-related studies throughout the publication periods. Over the periods, scholars have developed DEA applications in several categories, such as bootstrap, networking. Meanwhile, GMM with different approach (dynamic and system) techniques exploited panel data primarily extracted from Bankscope, Datastream, annual reports etc.

Main findings

Earlier, banking inefficiencies were substantially observed low, negatively affecting profitability and marketability [ 50 , 51 ]. This trend was continuously depicted in studies [ 52 ]. However, Berger et al. evidenced better efficiency for larger banks than smaller banks [ 50 ]. On the contrary, Seiford and Zhu posited an adverse effect of bank size on marketability [ 51 ]. More so, Rehman et al. found larger banks are less efficient than smaller banks [ 40 ]. Hence, Moudud-Ul-Huq posited diverse impacts of bank size and competition on performance [ 62 ]. So, banking size is deemed to have a substantial effect on the overall performance of banks. However, Adesina embellished that diversification of services and choices of management decisions on loans (nonperforming, debt issuances) [ 63 , 64 ] and deposits [ 41 ] affect the banking performance [ 37 ]. Moreover, board structure affects banking performance [ 40 , 65 ], while higher human capital efficiency enhances banking performance [ 37 ].

Generally, foreign-owned banks provide better service, greater profitability and are better efficient than local banks. This phenomenon was evidenced in several studies; for example, Bonin et al. and other scholars demonstrated that foreign-owned banks are more cost-efficient than other banks [ 48 ]. However, this trend did not exist for Islamic banks as local banks showed better efficiency than foreign peers [ 58 ] and more efficient than conventional [ 59 ]. Meanwhile, state-owned or government-owned commercial banks were less efficient and provided poorer services [ 48 , 49 , 52 ]. But these banks’ efficiency was higher than urban/rural banks during credit risk shock [ 41 ]. Nevertheless, banking efficiency and performance substantially depend on diversification of services, managerial adequacy, ownership, types and size.

Studies have evidenced financial development and thus the banking industry’s role in economic growth [ 60 ]. In the nineteenth century, the establishment of the savings bank demonstrated city growth in Prussia [ 66 ]. Potentially, banks provide investment capital to increase per capita GDP [ 43 ]. However, Haini documented a contrasting effect of banking development on economic growth through a push out of private investment due to high levels of the banking sector [ 67 ]. However, Stewart and Chowdhury proved that a stable banking sector lessens the negative impact of a crisis on GDP growth and provides economic resilience in both developed and developing countries. Overall, findings elaborated a crucial link between banking sector development and economic growth.

Future study suggestions

This study has recommended several scopes for future studies in the hybrid review, mainly through bibliometric findings and the structured review of impactful articles [ 11 ]. In other words, the recommendations for future studies are made by observing and analysing discussions on highly cited and recent cited documents. Overall findings and analyses raised several questions that need to be addressed for future studies.

Firstly, does the banking sector improve economic growth in the least developed countries? Prior studies mainly focused on developed and developing economies, but less attention was given to least developed countries. Secondly, vast studies investigated contributing factors of banking performance, while political instability has been ignored. Future studies might include political instability on the banking performance. Apart from it, nonperforming loans can be another addition to future studies, and even few studies documented it. Thirdly, how do banks perform during the pandemic crisis, for instance, COVID-19? The current pandemic crisis can be a significant factor in banking performance related to future studies, including efficiency, mortgages, loan recovery, deposits and business services. The studies can include consumer behaviour (due to restricted movements, safety measurements), green banking (online transaction and services), financial technologies (inclusion of nonbanking services) and the contribution or continuance of economic activities in the country during and after the pandemic crisis.

Significantly, prior studies have ignored the current trend of FinTech inclusion in banking performance. Fourthly, will FinTech takeover the banking services and diminish banks in the near future? Future studies may investigate the effect of FinTech applications on banking. More so, future studies may explore the banking industry’s barriers, challenges and threats due to FinTech growth. Fifthly, almost all studies employed quantitative analysis related to banking performance. Therefore, future studies may use qualitative methods to explore the opportunities and practices of banks and their performance. Sixthly, the majority of the studies either applied parametric or econometric techniques to investigate the bank performance. Recent developments in technologies and methods may provide easy and robust results in such related studies as using machine learning for data analysis and predicting banking efficiency and productivity determinants. Seventhly, past studies mostly followed the intermediation approach, which scarcely included production and operating approach measurement. Future studies may extend the efficiency analysis using productivity growth analysis. Further, the majority of the studies observed efficiency only. Future studies can include a productivity change index along with an efficiency analysis. Finally, GMM and regression were broadly applied to investigate the effect of antecedents of banking performance and link to economic growth. Future studies may adopt other advanced data analysis techniques such as partial least squares, structural equations and other econometric techniques.

Conclusions

The main purpose of this study is to explore the trends and research activities in banking performance and the economic growth research domain. To achieve this objective, a bibliometric analysis was applied and performed several analyses, namely citation, co-occurrence of keywords, the collaboration between authors and coupling between institutions and countries, and discussion by reviewing most cited and most recent influential research articles. This study presents the most common themes, sub-themes associated with highly cited documents and authors; furthermore, the content analysis identified the research directions, research objectives, methodologies, topics and findings.

Based on the reviewing literature, the efficiency theory, banking theory mainly intermediation approach and nonparametric technique, namely data envelopment analysis along with econometric method, regression was used in the published documents. The findings of this study, along with future study suggestions, could be beneficial to bankers as well as academic researchers and students studying banking performance and its role in the economy.

Limitations

The most crucial limitation in any bibliometric analysis is the database selection. It means selecting the data and the limits of its interpretation [ 68 ]. This study has three key limitations; firstly, it has chosen ‘Web of Science’, one of the largest online databases to gather data on banking performance research articles from 1972 to 2021 and refined based on subject categories and language (English). The database could be improved if other databases were included and also if book chapters and conference proceedings were added. Secondly, the selection of keywords; although selected keywords are deemed to be most relevant to encompass the majority of articles related to banking performance, there is always an opportunity to search further articles by using additional keywords. Lastly, this study could not conduct co-citation analysis due to the unavailability of cited documents in Web of Science data format.

Availability of data and materials

The data collected from the Web of Science online database were saved on Microsoft excel and remained with authors. The data are available upon request.

Abbreviations

Data envelopment analysis

Generalized method of moments

  • Web of Science

Collaboration index

Chief executive officer

Corporate social responsibility

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Alam, S.M.S., Chowdhury, M.A.M. & Razak, D.B.A. Research evolution in banking performance: a bibliometric analysis. Futur Bus J 7 , 66 (2021). https://doi.org/10.1186/s43093-021-00111-7

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Deep learning in finance and banking: A literature review and classification

  • Jian Huang 1 ,
  • Junyi Chai   ORCID: orcid.org/0000-0003-1560-845X 2 &
  • Stella Cho 2  

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Deep learning has been widely applied in computer vision, natural language processing, and audio-visual recognition. The overwhelming success of deep learning as a data processing technique has sparked the interest of the research community. Given the proliferation of Fintech in recent years, the use of deep learning in finance and banking services has become prevalent. However, a detailed survey of the applications of deep learning in finance and banking is lacking in the existing literature. This study surveys and analyzes the literature on the application of deep learning models in the key finance and banking domains to provide a systematic evaluation of the model preprocessing, input data, and model evaluation. Finally, we discuss three aspects that could affect the outcomes of financial deep learning models. This study provides academics and practitioners with insight and direction on the state-of-the-art of the application of deep learning models in finance and banking.

Introduction

Deep learning (DL) is an advanced technique of machine learning (ML) based on artificial neural network (NN) algorithms. As a promising branch of artificial intelligence, DL has attracted great attention in recent years. Compared with conventional ML techniques such as support vector machine (SVM) and k-nearest neighbors (kNN), DL possesses advantages of the unsupervised feature learning, a strong capability of generalization, and a robust training power for big data. Currently, DL has been applied comprehensively in classification and prediction tasks, computer visions, image processing, and audio-visual recognition (Chai and Li 2019 ). Although DL was developed in the field of computer science, its applications have penetrated diversified fields such as medicine, neuroscience, physics and astronomy, finance and banking (F&B), and operations management (Chai et al. 2013 ; Chai and Ngai 2020 ). The existing literature lacks a good overview of DL applications in F&B fields. This study attempts to bridge this gap.

While DL is the focus of computer vision (e.g., Elad and Aharon 2006 ; Guo et al. 2016 ) and natural language processing (e.g., Collobert et al. 2011 ) in the mainstream, DL applications in F&B are developing rapidly. Shravan and Vadlamani (2016) investigated the tools of text mining for F&B domains. They examined the representative ML algorithms, including SVM, kNN, genetic algorithm (GA), and AdaBoost. Butaru et al. ( 2016 ) compared performances of DL algorithms, including random forests, decision trees, and regularized logistic regression. They found that random forests gained the highest classification accuracy in the delinquency status.

Cavalcante et al. ( 2016 ) summarized the literature published from 2009 to 2015. They analyzed DL models, including multi-layer perceptron (MLP) (a fast library for approximate nearest neighbors), Chebyshev functional link artificial NN, and adaptive weighting NN. Although the study constructed a prediction framework in financial trading, some notable DL techniques such as long short-term memory (LSTM) and reinforcement learning (RL) models are neglect. Thus, the framework cannot ascertain the optimal model in a specific condition.

The reviews of the existing literature are either incomplete or outdated. However, our study provides a comprehensive and state-of-the-art review that could capture the relationships between typical DL models and various F&B domains. We identified critical conditions to limit our collection of articles. We employed academic databases in Science Direct, Springer-Link Journal, IEEE Xplore, Emerald, JSTOR, ProQuest Database, EBSCOhost Research Databases, Academic Search Premier, World Scientific Net, and Google Scholar to search for articles. We used two groups of keywords for our search. One group is related to the DL, including “deep learning,” “neural network,” “convolutional neural networks” (CNN), “recurrent neural network” (RNN), “LSTM,” and “RL.” The other group is related to finance, including “finance,” “market risk,” “stock risk,” “credit risk,” “stock market,” and “banking.” It is important to conduct cross searches between computer-science-related and finance-related literature. Our survey exclusively focuses on the financial application of DL models rather than other DL models like SVM, kNN, or random forest. The time range of our review was set between 2014 and 2018. In this stage, we collected more than 150 articles after cross-searching. We carefully reviewd each article and considered whether it is worthy of entering our pool of articles for review. We removed the articles if they are not from reputable journals or top professional conferences. Moreover, articles were discarded if the details of financial DL models presented were not clarified. Thus, 40 articles were selected for this review eventually.

This study contributes to the literature in the following ways. First, we systematically review the state-of-the-art applications of DL in F&B fields. Second, we summarize multiple DL models regarding specified F&B domains and identify the optimal DL model of various application scenarios. Our analyses rely on the data processing methods of DL models, including preprocessing, input data, and evaluation rules. Third, our review attempts to bridge the technological and application levels of DL and F&B, respectively. We recognize the features of various DL models and highlight their feasibility toward different F&B domains. The penetration of DL into F&B is an emerging trend. Researchers and financial analysts should know the feasibilities of particular DL models toward a specified financial domain. They usually face difficulties due to the lack of connections between core financial domains and numerous DL models. This study will fill this literature gap and guide financial analysts.

The rest of this paper is organized as follows. Section 2 provides a background of DL techniques. Section 3 introduces our research framework and methodology. Section 4 analyzes the established DL models. Section 5 analyzes key methods of data processing, including data preprocessing and data inputs. Section 6 captures appeared criteria for evaluating the performance of DL models. Section 7 provides a general comparison of DL models against identified F&B domains. Section 8 discusses the influencing factors in the performance of financial DL models. Section 9 concludes and outlines the scope for promising future studies.

Background of deep learning

Regarding DL, the term “deep” presents the multiple layers that exist in the network. The history of DL can be traced back to stochastic gradient descent in 1952, which is employed for an optimization problem. The bottleneck of DL at that time was the limit of computer hardware, as it was very time-consuming for computers to process the data. Today, DL is booming with the developments of graphics processing units (GPUs), dataset storage and processing, distributed systems, and software such as Tensor Flow. This section briefly reviews the basic concept of DL, including NN and deep neural network (DNN). All of these models have greatly contributed to the applications in F&B.

The basic structure of NN can be illustrated as Y  =  F ( X T w  +  c ) regarding the independent (input) variables X , the weight terms w , and the constant terms c . Y is the dependent variable and X is formed as an n  ×  m matrix for the number of training sample n and the number of input variables m . To apply this structure in finance, Y can be considered as the price of next term, the credit risk level of clients, or the return rate of a portfolio. F is an activation function that is unique and different from regression models. F is usually formulated as sigmoid functions and tanh functions. Other functions can also be used, including ReLU functions, identity functions, binary step functions, ArcTan functions, ArcSinh functions, ISRU functions, ISRLU functions, and SQNL functions. If we combine several perceptrons in each layer and add a hidden layer from Z 1 to Z 4 in the middle, we term a single layer as a neural network, where the input layers are the X s , and the output layers are the Y s . In finance, Y can be considered as the stock price. Moreover, multiple Y s are also applicable; for instance, fund managers often care about future prices and fluctuations. Figure  1 illustrates the basic structure.

figure 1

The structure of NN

Based on the basic structure of NN shown in Fig.  1 , traditional networks include DNN, backpropagation (BP), MLP, and feedforward neural network (FNN). Using these models can ignore the order of data and the significance of time. As shown in Fig.  2 , RNN has a new NN structure that can address the issues of long-term dependence and the order between input variables. As financial data in time series are very common, uncovering hidden correlations is critical in the real world. RNN can be better at solving this problem, as compared to other moving average (MA) methods that have been frequently adopted before. A detailed structure of RNN for a sequence over time is shown in Part B of the Appendix (see Fig. 7 in Appendix ).

figure 2

The abstract structure of RNN

Although RNN can resolve the issue of time-series order, the issue of long-term dependencies remains. It is difficult to find the optimal weight for long-term data. LSTM, as a type of RNN, added a gated cell to overcome long-term dependencies by combining different activation functions (e.g., sigmoid or tanh). Given that LSTM is frequently used for forecasting in the finance literature, we extract LSTM from RNN models and name other structures of standard RNN as RNN(O).

As we focus on the application rather than theoretical DL aspect, this study will not consider other popular DL algorithms, including CNN and RL, as well as Latent variable models such as variational autoencoders and generative adversarial network. Table 6 in Appendix shows a legend note to explain the abbreviations used in this paper. We summarize the relationship between commonly used DL models in Fig.  3 .

figure 3

Relationships of reviewed DL models for F&B domains

Research framework and methodology

Our research framework is illustrated in Fig.  4 . We combine qualitative and quantitative analyses of the articles in this study. Based on our review, we recognize and identify seven core F&B domains, as shown in Fig.  5 . To connect the DL side and the F&B side, we present our review on the application of the DL model in seven F&B domains in Section 4. It is crucial to analyze the feasibility of a DL model toward particular domains. To do so, we provide summarizations in three key aspects, including data preprocessing, data inputs, and evaluation rules, according to our collection of articles. Finally, we determine optimal DL models regarding the identified domains. We further discuss two common issues in using DL models for F&B: overfitting and sustainability.

figure 4

The research framework of this study

figure 5

The identified domains of F&B for DL applications

Figure  5 shows that the application domains can be divided into two major areas: (1) banking and credit risk and (2) financial market investment. The former contains two domains: credit risk prediction and macroeconomic prediction. The latter contains financial prediction, trading, and portfolio management. Prediction tasks are crucial, as emphasized by Cavalcante et al. ( 2016 ). We study this domain from three aspects of prediction, including exchange rate, stock market, and oil price. We illustrate this structure of application domains in F&B.

Figure  6 shows a statistic in the listed F&B domains. We illustrate the domains of financial applications on the X-axis and count the number of articles on the Y-axis. Note that a reviewed article could cover more than one domain in this figure; thus, the sum of the counts (45) is larger than the size of our review pool (40 articles). As shown in Fig.  6 , stock marketing prediction and trading dominate the listed domains, followed by exchange rate prediction. Moreover, we found two articles on banking credit risk and two articles on portfolio management. Price prediction and macroeconomic prediction are two potential topics that deserve more studies.

figure 6

A count of articles over seven identified F&B domains

Application of DL models in F&B domains

Based on our review, six types of DL models are reported. They are FNN, CNN, RNN, RL, deep belief networks (DBN), and restricted Boltzmann machine (RBM). Regarding FNN, several papers use the alternative terms of backpropagation artificial neural network (ANN), FNN, MLP, and DNN. They have an identical structure. Regarding RNN, one of its well-known models in the time-series analysis is called LSTM. Nearly half of the reviewed articles apply FNN as the primary DL technique. Nine articles apply LSTM, followed by eight articles for RL, and six articles for RNN. Minor ones that are applied in F&B include CNN, DBM, and RBM. We count the number of articles that use various DL models in seven F&B domains, as shown in Table  1 . FNN is the principal model used in exchange rate, price, and macroeconomic predictions, as well as banking default risk and credit. LSTM and FNN are two kinds of popular models for stock market prediction. Differently, RL and FNN are frequently used regarding stock trading. FNN, RL, and simple RNN can be conducted in portfolio management. FNN is the primary model in macroeconomic and banking risk prediction. CNN, LSTM, and RL are emerging research approaches in banking risk prediction. The detailed statistics that contain specific articles can be found in Table 5 in Appendix .

Exchange rate prediction

Shen et al. ( 2015 ) construct an improved DBN model by including RBM and find that their model outperforms the random walk algorithm, auto-regressive-moving-average (ARMA), and FNN with fewer errors. Zheng et al. ( 2017 ) examine the performance of DBN and find that the DBN model estimates the exchange rate better than FNN model does. They find that a small number of layer nodes engender a more significant effect on DBN.

Several scholars believe that a hybrid model should have better performance. Ravi et al. ( 2017 ) contribute a hybrid model by using MLP (FNN), chaos theory, and multi-objective evolutionary algorithms. Their Chaos+MLP + NSGA-II model Footnote 1 has a mean squared error (MSE) with 2.16E-08 that is very low. Several articles point out that only a complicated neural network like CNN can gain higher accuracy. For example, Galeshchuk and Mukherjee ( 2017 ) conduct experiments and claim that a single hidden layer NN or SVM performs worse than a simple model like moving average (MA). However, they find that CNN could achieve higher classification accuracy in predicting the direction of the change of exchange rate because of successive layers of DNN.

Stock market prediction

In stock market prediction, some studies suggest that market news may influence the stock price and DL model, such as using a magic filter to extract useful information for price prediction. Matsubara et al. ( 2018 ) extract information from the news and propose a deep neural generative model to predict the movement of the stock price. This model combines DNN and a generative model. It suggests that this hybrid approach outperforms SVM and MLP.

Minh et al. ( 2017 ) develop a novel framework with two streams combining the gated recurrent unit network and the Stock2vec. It employs a word embedding and sentiment training system on financial news and the Harvard IV-4 dataset. They use the historical price and news-based signals from the model to predict the S&P500 and VN-index price directions. Their model shows that the two-stream gated recurrent unit is better than the gated recurrent unit or the LSTM. Jiang et al. ( 2018 ) establish a recurrent NN that extracts the interaction between the inner-domain and cross-domain of financial information. They prove that their model outperforms the simple RNN and MLP in the currency and stock market. Krausa and Feuerriegel ( 2017 ) propose that they can transform financial disclosure into a decision through the DL model. After training and testing, they point out that LSTM works better than the RNN and conventional ML methods such as ridge regression, Lasso, elastic net, random forest, SVR, AdaBoost, and gradient boosting. They further pre-train words embeddings with transfer learning (Krausa and Feuerriegel 2017 ). They conclude that better performance comes from LSTM with word embeddings. In the sentiment analysis, Sohangir et al. ( 2018 ) compares LSTM, doc2vec, and CNN to evaluate the stock opinions on the StockTwits. They conclude that CNN is the optimal model to predict the sentiment of authors. This result may be further applied to predict the stock market trend.

Data preprocessing is conducted to input data into the NN. Researchers may apply numeric unsupervised methods of feature extraction, including principal component analysis, autoencoder, RBM, and kNN. These methods can reduce the computational complexity and prevent overfitting. After the input of high-frequency transaction data, Chen et al. ( 2018b ) establish a DL model with an autoencoder and an RBM. They compare their model with backpropagation FNN, extreme learning machine, and radial basis FNN. They claim that their model can better predict the Chinese stock market. Chong et al. ( 2017 ) apply the principal component analysis (PCA) and RBM with high-frequency data of the South Korean market. They find that their model can explain the residual of the autoregressive model. The DL model can thus extract additional information and improve prediction performance. More so, Singh and Srivastava ( 2017 ) describe a model involving 2-directional and 2-dimensional (2D 2 ) PCA and DNN. Their model outperforms 2D 2 with radial basis FNN and RNN.

For time-series data, sometimes it is difficult to judge the weight of long-term and short-term data. The LSTM model is just for resolving this problem in financial prediction. The literature has attempted to prove that LSTM models are applicable and outperform conventional FNN models. Yan and Ouyang ( 2017 ) apply LSTM to challenge the MLP, SVM, and kNN in predicting a static and dynamic trend. After a wavelet decomposition and a reconstruction of the financial time series, their model can be used to predict a long-term dynamic trend. Baek and Kim ( 2018 ) apply LSTM not only in predicting the price of S&P500 and KOSPI200 but also in preventing overfitting. Kim and Won ( 2018 ) apply LSTM in the prediction of stock price volatility. They propose a hybrid model that combines LSTM with three generalized autoregressive conditional heteroscedasticity (GARCH)-type models. Hernandez and Abad ( 2018 ) argue that RBM is inappropriate for dynamic data modeling in the time-series analysis because it cannot retain memory. They apply a modified RBM model called p -RBM that can retain the memory of p past states. This model is used in predicting market directions of the NASDAQ-100 index. Compared with vector autoregression (VAR) and LSTM, notwithstanding, they find that LSTM is better because it can uncover the hidden structure within the non-linear data while VAR and p -RBM cannot capture the non-linearity in data.

CNN was established to predict the price with a complicated structure. Making the best use of historical price, Dingli and Fournier ( 2017 ) develop a new CNN model. This model can predict next month’s price. Their results cannot surpass other comparable models, such as logistic regression (LR) and SVM. Tadaaki ( 2018 ) applies the financial ratio and converts them into a “grayscale image” in the CNN model. The results reveal that CNN is more efficient than decision trees (DT), SVM, linear discriminant analysis, MLP, and AdaBoost. To predict the stock direction, Gunduz et al. ( 2017 ) establish a CNN model with a so-called specially ordered feature set whose classifier outperforms either CNN or LR.

Stock trading

Many studies adopt the conventional FNN model and try to set up a profitable trading system. Sezer et al. ( 2017 ) combine GA with MLP. Chen et al. ( 2017 ) adopt a double-layer NN and discover that its accuracy is better than ARMA-GARCH and single-layer NN. Hsu et al. ( 2018 ) equip the Black-Scholes model and a three-layer fully-connected feedforward network to estimate the bid-ask spread of option price. They argue that this novel model is better than the conventional Black-Scholes model with lower RMSE. Krauss et al. ( 2017 ) apply DNN, gradient-boosted-trees, and random forests in statistical arbitrage. They argue that their returns outperform the market index S&P500.

Several studies report that RNN and its derivate models are potential. Deng et al. ( 2017 ) extend the fuzzy learning into the RNN model. After comparing their model to different DL models like CNN, RNN, and LSTM, they claim that their model is the optimal one. Fischer and Krauss ( 2017 ) and Bao et al. ( 2017 ) argue that LSTM can create an optimal trading system. Fischer and Krauss ( 2017 ) claim that their model has a daily return of 0.46 and a sharp ratio of 5.8 prior to the transaction cost. Given the transaction cost, however, LSTM’s profitability fluctuated around zero after 2010. Bao et al. ( 2017 ) advance Fischer and Krauss’s ( 2017 ) work and propose a novel DL model (i.e., WSAEs-LSTM model). It uses wavelet transforms to eliminate noise, stacked autoencoders (SAEs) to predict stock price, and LSTM to predict the close price. The result shows that their model outperforms other models such as WLSTM, Footnote 2 LSTM, and RNN in predictive accuracy and profitability.

RL is popular recently despite its complexity. We find that five studies apply this model. Chen et al. ( 2018a ) propose an agent-based RL system to mimic 80% professional trading strategies. Feuerriegel and Prendinger ( 2016 ) convert the news sentiment into the signal in the trading system, although their daily returns and abnormal returns are nearly zero. Chakraborty ( 2019 ) cast the general financial market fluctuation into a stochastic control problem and explore the power of two RL models, including Q-learning Footnote 3 and state-action-reward-state-action (SARSA) algorithm. Both models can enhance profitability (e.g., 9.76% for Q-learning and 8.52% for SARSA). They outperform the buy-and-hold strategy. Footnote 4 Zhang and Maringer ( 2015 ) conduct a hybrid model called GA, with recurrent RL. GA is used to select an optimal combination of technical indicators, fundamental indicators, and volatility indicators. The out-of-sample trading performance is improved due to a significantly positive Sharpe ratio. Martinez-Miranda et al. ( 2016 ) create a new topic of trading. It uses a market manipulation scanner model rather than a trading system. They use RL to model spoofing-and-pinging trading. This study reveals that their model just works on the bull market. Jeong and Kim ( 2018 ) propose a model called deep Q-network that is constructed by RL, DNN, and transfer learning. They use transfer learning to solve the overfitting issue incurred as a result of insufficient data. They argue that the profit yields in this system increase by four times the amount in S&P500, five times in KOSPI, six times in EuroStoxx50, and 12 times in HIS.

Banking default risk and credit

Most articles in this domain focus on FNN applications. Rönnqvist and Sarlin ( 2017 ) propose a model for detecting relevant discussions in texting and extracting natural language descriptions of events. They convert the news into a signal of the bank-distress report. In their back-test, their model reflects the distressing financial event of the 2007–2008 period.

Zhu et al. ( 2018 ) propose a hybrid CNN model with a feature selection algorithm. Their model outperforms LR and random forest in consumer credit scoring. Wang et al. ( 2019 ) consider that online operation data can be used to predict consumer credit scores. They thus convert each kind of event into a word and apply the Event2vec model to transform the word into a vector in the LSTM network. The probability of default yields higher accuracy than other models. Jurgovsky et al. ( 2018 ) employs the LSTM to detect credit card fraud and find that LSTM can enhance detection accuracy.

Han et al. ( 2018 ) report a method that adopts RL to assess the credit risk. They claim that high-dimensional partial differential equations (PDEs) can be reformulated by using backward stochastic differential equations. NN approximates the gradient of the unknown solution. This model can be applied to F&B risk evaluation after considering all elements such as participating agents, assets, and resources, simultaneously.

Portfolio management

Song et al. ( 2017 ) establish a model after combining ListNet and RankNet to make a portfolio. They take a long position for the top 25% stocks and hold the short position for the bottom 25% stocks weekly. The ListNetlong-short model is the optimal one, which can achieve a return of 9.56%. Almahdi and Yang ( 2017 ) establish a better portfolio with a combination of RNN and RL. The result shows that the proposed trading system respond to transaction cost effects efficiently and outperform hedge fund benchmarks consistently.

Macroeconomic prediction

Sevim et al. ( 2014 ) develops a model with a back-propagation learning algorithm to predict the financial crises up to a year before it happened. This model contains three-layer perceptrons (i.e., MLP) and can achieve an accuracy rate of approximately 95%, which is superior to DT and LR. Chatzis et al. ( 2018 ) examine multiple models such as classification tree, SVM, random forests, DNN, and extreme gradient boosting to predict the market crisis. The results show that crises encourage persistence. Furthermore, using DNN increases the classification accuracy that makes global warning systems more efficient.

Price prediction

For price prediction, Sehgal and Pandey ( 2015 ) review ANN, SVM, wavelet, GA, and hybrid systems. They separate the time-series models into stochastic models, AI-based models, and regression models to predict oil prices. They reveal that researchers prevalently use MLP for price prediction.

Data preprocessing and data input

Data preprocessing.

Data preprocessing is conducted to denoise before data training of DL. This section summarizes the methods of data preprocessing. Multiple preprocessing techniques discussed in Part 4 include the principal component analysis (Chong et al. 2017 ), SVM (Gunduz et al. 2017 ), autoencoder, and RBM (Chen et al. 2018b ). There are several additional techniques of feature selection as follows.

Relief: The relief algorithm (Zhu et al. 2018 ) is a simple approach to weigh the importance of the feature. Based on NN algorithms, relief repeats the process for n times and divides each final weight vector by n . Thus, the weight vectors are the relevance vectors, and features are selected if their relevance is larger than the threshold τ .

Wavelet transforms: Wavelet transforms are used to fix the noise feature of the financial time series before feeding into a DL network. It is a widely used technique for filtering and mining single-dimensional signals (Bao et al. 2017 ).

Chi-square: Chi-square selection is commonly used in ML to measure the dependence between a feature and a class label. The representative usage is by Gunduz et al. ( 2017 ).

Random forest: Random forest algorithm is a two-stage process that contains random feature selection and bagging. The representative usage is by Fischer and Krauss ( 2017 ).

Data inputs

Data inputs are an important criterion for judging whether a DL model is feasible for particular F&B domains. This section summarizes the method of data inputs that have been adopted in the literature. Based on our review, five types of input data in the F&B domain can be presented. Table  2 provides a detailed summary of the input variable in F&B domains.

History price: The daily exchange rate can be considered as history price. The price can be the high, low, open, and close price of the stock. Related articles include Bao et al. ( 2017 ), Chen et al. ( 2017 ), Singh and Srivastava ( 2017 ), and Yan and Ouyang ( 2017 ).

Technical index: Technical indexes include MA, exponential MA, MA convergence divergence, and relative strength index. Related articles include Bao et al. ( 2017 ), Chen et al. ( 2017 ), Gunduz et al. ( 2017 ), Sezer et al. ( 2017 ), Singh and Srivastava ( 2017 ), and Yan and Ouyang ( 2017 ).

Financial news: Financial news covers financial message, sentiment shock score, and sentiment trend score. Related articles include Feuerriegel and Prendinger ( 2016 ), Krausa and Feuerriegel ( 2017 ), Minh et al. ( 2017 ), and Song et al. ( 2017 ).

Financial report data: Financial report data can account for items in the financial balance sheet or the financial report data (e.g., return on equity, return on assets, price to earnings ratio, and debt to equity ratio). Zhang and Maringer ( 2015 ) is a representative study on the subject.

Macroeconomic data: This kind of data includes macroeconomic variables. It may affect elements of the financial market, such as exchange rate, interest rate, overnight interest rate, and gross foreign exchange reserves of the central bank. Representative articles include Bao et al. ( 2017 ), Kim and Won ( 2018 ), and Sevim et al. ( 2014 ).

Stochastic data: Chakraborty ( 2019 ) provides a representative implementation.

Evaluation rules

It is critical to judge whether an adopted DL model works well in a particular financial domain. We, thus, need to consider evaluation systems of criteria for gauging the performance of a DL model. This section summarizes the evaluation rules of F&B-oriented DL models. Based on our review, three evaluation rules dominate: the error term, the accuracy index, and the financial index. Table  3 provides a detailed summary. The evaluation rules can be boiled down to the following categories.

Error term: Suppose Y t  +  i and F t  +  i are the real data and the prediction data, respectively, where m is the total number. The following is a summary of the functional formula commonly employed for evaluating DL models.

Mean Absolute Error (MAE): \( {\sum}_{i=1}^m\frac{\left|{Y}_{t+i}-{F}_{t+i}\right|}{m} \) ;

Mean Absolute Percent Error (MAPE): \( \frac{100}{m}{\sum}_{i=1}^m\frac{\left|{Y}_{t+i}-{F}_{t+i}\right|}{Y_{t+i}} \) ;

Mean Squared Error (MSE): \( {\sum}_{i=1}^m\frac{{\left({Y}_{t+i}-{F}_{t+i}\right)}^2}{m} \) ;

Root Mean Squared Error (RMSE): \( \sqrt{\sum_{i=1}^m\frac{{\left({Y}_{t+i}-{F}_{t+i}\right)}^2}{m}} \) ;

Normalized Mean Square Error (NMSE): \( \frac{1}{m}\frac{\sum {\left({Y}_{t+i}-{F}_{t+i}\right)}^2}{\mathit{\operatorname{var}}\left({Y}_{t+i}\right)} \) .

Accuracy index: According to Matsubara et al. ( 2018 ), we use TP, TN, FP, and FN to represent the number of true positives, true negatives, false positives, and false negatives, respectively, in a confusion matrix for classification evaluation. Based on our review, we summarize the accuracy indexes as follows.

Directional Predictive Accuracy (DPA): \( \frac{1}{N}{\sum}_{t=1}^N{D}_t \) , if ( Y t  + 1  −  Y t ) × ( F t  + 1  −  Y t ) ≥ 0, D t  = 1, otherwise, D t  = 0;

Actual Correlation Coefficient (ACC): \( \frac{TP+ TN}{TP+ FP+ FN+ TN} \) ;

Matthews Correlation Coefficient (MCC): \( \frac{TP\times TN- FP\times FN}{\sqrt{\left( TP+ FP\right)\left( TP+ FN\right)\left( TN+ FP\right)\left( TN+ FN\right)}} \) .

Financial index: Financial indexes involve total return, Sharp ratio, abnormal return, annualized return, annualized number of transaction, percentage of success, average profit percent per transaction, average transaction length, maximum profit percentage in the transaction, maximum loss percentage in the transaction, maximum capital, and minimum capital.

For the prediction by regressing the numeric dependent variables (e.g., exchange rate prediction or stock market prediction), evaluation rules are mostly error terms. For the prediction by classification in the category data (e.g., direction prediction on oil price), the accuracy indexes are widely conducted. For stock trading and portfolio management, financial indexes are the final evaluation rules.

General comparisons of DL models

This study identifies the most efficient DL model in each identified F&B domain. Table  4 illustrates our comparisons of the error terms in the pool of reviewed articles. Note that “A > B” means that the performance of model A is better than that of model B. “A + B” indicates the hybridization of multiple DL models.

At this point, we have summarized three methods of data processing in DL models against seven specified F&B domains, including data preprocessing, data inputs, and evaluation rules. Apart from the technical level of DL, we find the following:

NN has advantages in handling cross-sectional data;

RNN and LSTM are more feasible in handling time series data;

CNN has advantages in handling the data with multicollinearity.

Apart from application domains, we can induce the following viewpoints. Cross-sectional data usually appear in exchange rate prediction, price prediction, and macroeconomic prediction, for which NN could be the most feasible model. Time series data usually appear in stock market prediction, for which LSTM and RNN are the best options. Regarding stock trading, a feasible DL model requires the capabilities of decision and self-learning, for which RL can be the best. Moreover, CNN is more suitable for the multivariable environment of any F&B domains. As shown in the statistics of the Appendix , the frequency of using corresponding DL models corresponds to our analysis above. Selecting proper DL models according to the particular needs of financial analysis is usually challenging and crucial. This study provides several recommendations.

We summarize emerging DL models in F&B domains. Nevertheless, can these models refuse the efficient market hypothesis (EMH)? Footnote 5 According to the EMH, the financial market has its own discipline. There is no long-term technical tool that could outperform an efficient market. If so, using DL models may not be practical in long-term trading as it requires further experimental tests. However, why do most of the reviewed articles argue that their DL models of trading outperform the market returns? This argument has challenged the EMH. A possible explanation is that many DL algorithms are still challenging to apply in the real-world market. The DL models may raise trading opportunities to gain abnormal returns in the short-term. In the long run, however, many algorithms may lose their superiority, whereas EMH still works as more traders recognize the arbitrage gap offered by these DL models.

This section discusses three aspects that could affect the outcomes of DL models in finance.

Training and validation of data processing

The size of the training set.

The optimal way to improve the performance of models is by enhancing the size of the training data. Bootstrap can be used for data resampling, and generative adversarial network (GAN) can extend the data features. However, both can recognize numerical parts of features. Sometimes, the sample set is not diverse enough; thus, it loses its representativeness. Expanding the data size could make the model more unstable. The current literature reported diversified sizes of training sets. The requirements of data size in the training stage could vary by different F&B tasks.

The number of input factors

Input variables are independent variables. Based on our review, multi-factor models normally perform better than single-factor models in the case that the additional input factors are effective. In the time-series data model, long-term data have less prediction errors than that for a short period. The number of input factors depends on the employment of the DL structure and the specific environment of F&B tasks.

The quality of data

Several methods can be used to improve the data quality, including data cleaning (e.g., dealing with missing data), data normalization (e.g., taking the logarithm, calculating the changes of variables, and calculating the t -value of variables), feature selection (e.g., Chi-square test), and dimensionality reduction (e.g., PCA). Financial DL models require that the input variables should be interpretable in economics. When inputting the data, researchers should clarify the effective variables and noise. Several financial features, such as technical indexes, are likely to be created and added into the model.

Selection on structures of DL models

DL model selection should depend on problem domains and cases in finance. NN is suitable for processing cross-sectional data. LSTM and other RNNs are optimal choices for time-series data in prediction tasks. CNN can settle the multicollinearity issue through data compression. Latent variable models like GAN can be better for dimension reduction and clustering. RL is applicable in the cases with judgments like portfolio management and trading. The return levels and outcomes on RL can be affected significantly by environment (observation) definitions, situation probability transfer matrix, and actions.

The setting of objective functions and the convexity of evaluation rules

Objective function selection affects training processes and expected outcomes. For predictions on stock price, low MAE merely reflects the effectiveness of applied models in training; however, it may fail in predicting future directions. Therefore, it is vital for additional evaluation rules for F&B. Moreover, it can be more convenient to resolve the objective functions if they are convex.

The influence of overfitting (underfitting)

Overfitting (underfitting) commonly happens in using DL models, which is clearly unfavorable. A generated model performs perfectly in one case but usually cannot replicate good performance with the same model and identical coefficients. To solve this problem, we have to trade off the bias against variances. Bias posits that researchers prefer to keep it small to illustrate the superiority of their models. Generally, a deeper (i.e., more layered) NN model or neurons can reduce errors. However, it is more time-consuming and could reduce the feasibility of applied DL models.

One solution is to establish validation sets and testing sets for deciding the numbers of layers and neurons. After setting optimal coefficients in the validation set (Chong et al. 2017 ; Sevim et al. 2014 ), the result in the testing sets reveals the level of errors that could mitigate the effect of overfitting. One can input more samples of financial data to check the stability of the model’s performance. This method is known as the early stopping. It stops training more layers in the network once the testing result has achieved an optimal level.

Moreover, regularization is another approach to conquer the overfitting. Chong et al. ( 2017 ) introduces a constant term for the objective function and eventually reduces the variates of the result. Dropout is also a simple method to address overfitting. It reduces the dimensions and layers of the network (Minh et al. 2017 ; Wang et al. 2019 ). Finally, the data cleaning process (Baek and Kim 2018 ; Bao et al. 2017 ), to an extent, could mitigate the impact of overfitting.

Financial models

The sustainability of the model.

According to our reviews, the literature focus on evaluating the performance of historical data. However, crucial problems remain. Given that prediction is always complicated, the problem of how to justify the robustness of the used DL models in the future remains. More so, whether a DL model could survive in dynamic environments must be considered.

The following solutions could be considered. First, one can divide the data into two groups according to the time range; performance can subsequently be checked (e.g., using the data for the first 3 years to predict the performance of the fourth year). Second, the feature selection can be used in the data preprocessing, which could improve the sustainability of models in the long run. Third, stochastic data can be generated for each input variable by fixing them with a confidence interval, after which a simulation to examine the robustness of all possible future situations is conducted.

The popularity of the model

Whether a DL model is effective for trading is subject to the popularity of the model in the financial market. If traders in the same market conduct an identical model with limited information, they may run identical results and adopt the same trading strategy accordingly. Thus, they may lose money because their strategy could sell at a lower price after buying at a higher.

Conclusion and future works

Concluding remarks.

This paper provides a comprehensive survey of the literature on the application of DL in F&B. We carefully review 40 articles refined from a collection of 150 articles published between 2014 and 2018. The review and refinement are based on a scientific selection of academic databases. This paper first recognizes seven core F&B domains and establish the relationships between the domains and their frequently-used DL models. We review the details of each article under our framework. Importantly, we analyze the optimal models toward particular domains and make recommendations according to the feasibility of various DL models. Thus, we summarize three important aspects, including data preprocessing, data inputs, and evaluation rules. We further analyze the unfavorable impacts of overfitting and sustainability when applying DL models and provide several possible solutions. This study contributes to the literature by presenting a valuable accumulation of knowledge on related studies and providing useful recommendations for financial analysts and researchers.

Future works

Future studies can be conducted from the DL technical and F&B application perspectives. Regarding the perspective of DL techniques, training DL model for F&B is usually time-consuming. However, effective training could greatly enhance accuracy by reducing errors. Most of the functions can be simulated with considerable weights in complicated networks. First, one of the future works should focus on data preprocessing, such as data cleaning, to reduce the negative effect of data noise in the subsequent stage of data training. Second, further studies on how to construct layers of networks in the DL model are required, particularly when considering a reduction of the unfavorable effects of overfitting and underfitting. According to our review, the comparisons between the discussed DL models do not hinge on an identical source of input data, which renders these comparisons useless. Third, more testing regarding F&B-oriented DL models would be beneficial.

In addition to the penetration of DL techniques in F&B fields, more structures of DL models should be explored. From the perspective of F&B applications, the following problems need further research to investigate desirable solutions. In the case of financial planning, can a DL algorithm transfer asset recommendations to clients according to risk preferences? In the case of corporate finance, how can a DL algorithm benefit capital structure management and, thus, maximize the values of corporations? How can managers utilize DL technical tools to gauge the investment environment and financial data? How can they use such tools to optimize cash balances and cash inflow and outflow? Until recently, DL models like RL and generative adversarial networks are rarely used. More investigations on constructing DL structures for F&B regarding preferences would be beneficial. Finally, the developments of professional F&B software and system platforms that implement DL techniques are highly desirable.

Availability of data and materials

Not applicable.

In the model, NSGA stands for non-dominated sorting genetic algorithm.

A combination of Wavelet transforms (WT) and long-short term memory (LSTM) is called WLSTM in Bao et al. ( 2017 ).

Q-learning is a model-free reinforcement learning algorithm.

Buy-and-hold is a passive investment strategy in which an investor buys stocks (or ETFs) and holds them for a long period regardless of fluctuations in the market.

EMH was developed from a Ph.D. dissertation by economist Eugene Fama in the 1960s. It says that at any given time, stock prices reflect all available information and trade at exactly their fair value at all times. It is impossible to consistently choose stocks that will beat the returns of the overall stock market. Therefore, this hypothesis implies that the pursuit of market-beating performance is more about chance than it is about researching and selecting the right stocks.

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Acknowledgments

The constructive comments of the editor and three anonymous reviewers on an earlier version of this paper are greatly appreciated. The authors are indebted to seminar participants at 2019 China Accounting and Financial Innovation Form at Zhuhai for insightful discussions. The corresponding author thanks the financial supports from BNU-HKBU United International College Research Grant under Grant R202026.

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Part A. Summary of publications in DL and F&B domains

Part b. detailed structure of standard rnn.

The abstract structure of RNN for a sequence cross over time can be extended, as shown in Fig. 7 in Appendix , which presents the inputs as X , the outputs as Y , the weights as w , and the Tanh functions.

figure 7

The detailed structure of RNN

Part C. List of abbreviations

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Insights into financial technology (FinTech): a bibliometric and visual study

  • Bo Li   ORCID: orcid.org/0000-0003-0721-0601 1 &
  • Zeshui Xu 2  

Financial Innovation volume  7 , Article number:  69 ( 2021 ) Cite this article

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This paper conducted a comprehensive analysis based on bibliometrics and science mapping analysis. First, 848 publications were obtained from Web of Science. Their fundamental characteristics were analyzed, including the types, annual publications, hot research directions, and foci (by theme analysis, co-occurrence analysis, and timeline analysis of author keywords). Next, the prolific objects (at the level of countries/regions, institutions, journals, and authors) and corresponding pivotal cooperative relationship networks were used to highlight who pays attention to FinTech. Furthermore, the citation structures of authors and journals were investigated, including citation and co-citation. Additionally, this paper presents the burst detection analysis of cited authors, journals, and references. Finally, combining the analysis results with the current financial environment, the challenges and future development opportunities are discussed further. Accordingly, a comprehensive study of the FinTech documents not only reviews the current research characteristics and trajectories but also helps scholars find the appropriate research entry point and conduct in-depth research.

Introduction

FinTech (abbreviation for financial technology, as an emerging technical term) is driven by a variety of emerging frontier technologies. It is a series of new business models, new technology applications, and new products and services that have a significant impact on the financial market and supply of financial services. It has attracted wide attention because of the following advantages: improving the efficiency of operations, reducing operating costs effectively, disrupting the existing industry structures, blurring industry boundaries, facilitating strategic disintermediation, providing new gateways for entrepreneurship, and democratizing access to financial services (Agarwal and Zhang 2020 ; Cao et al. 2020 ; Admati and Hellwig 2013 ; Loubere 2017 ; Pinochet et al. 2019 ; Philippon 2016 ; Yang et al. 2020 ; Suryono et al. 2020 ). The key technologies of FinTech include internet technology (including Internet and Web of Things) (Ruan et al. 2019 ), big data (Chen et al. 2017 ; Gai et al. 2018a ), artificial intelligence (Belanche et al. 2019 ), distributed technology (blockchain and cloud computing) (Belanche et al. 2019 ; Gomber et al. 2018 ; Chen et al. 2019 ; Wamba et al. 2020 ; Miau and Yang 2018 ), and security technology (biometric technology) (Gai et al. 2018a , b ; Wamba et al. 2020 ). Under the influence of these technologies, the traditional development model of the financial industry has changed.

Furthermore, scholars have done studies involving theories and applications. To examine FinTech adoption and use from the technology acceptance perspective, Singh et al. ( 2020 ) proposed a research framework by adding substructures of the technology acceptance model. The FinTech ecosystem consisting of FinTech startups, technology developers, government, financial customers, and traditional financial institutions was presented by Lee and Shin ( 2018 ). Accordingly, the application of FinTech has been involved in many areas, such as mobile payment (Gomber et al. 2018 ), mobile networks (Gai et al. 2016 ; Wen et al. 2013 ; Zhang et al. 2013 ; Zhang and Soong 2004 ), big data (Yin and Gai 2015 ), blockchain (Wamba et al. 2020 ; Iman 2018 ), P2P lending (Gomber et al. 2018 ; Ge et al. 2017 ; Suryono et al. 2021 ; Wang et al. 2020a , b ), cloud computing (Castiglione et al. 2015 ; Gai et al. 2018a , b ), banking service, investment funds, retail groups, and telecom operators (Singh et al. 2020 ), image processing (Castiglione et al. 2007 ), and data analysis techniques (Qiu et al. 2015 ).

FinTech promotes the development of the financial industry. Specifically, it will be easier to collect and analyze data in the financial market to reduce information asymmetry. Trading and investment strategies based on artificial intelligence and big data can redefine the price discovery mechanism of the financial market and improve transaction speed, promoting the liquidity of the financial market and enhancing the efficiency and stability of the financial market. Regulators analyze, warn, and prevent systemic risks in the financial market more efficiently. Additionally, the smart FinTech helps save labor costs and reduce staff duplication by combining big data with artificial intelligence. Next, the development and application of FinTech help more people, especially the poor, obtain financial services at a lower cost and more conveniently, and share more reform results. Moreover, because of the “Belt and Road”, many countries share the achievements of FinTech. For example, our country’s mobile payment helps the economic and financial development of countries along the “Belt and Road”.

To explore the boundaries and research paradigms of the financial disciplines that have been broken and reconstructed, this paper analyzed the current research characteristics and development trends according to the publications in the field of FinTech. 95.28% of all publications were published after 2015 (according to Web of Science (WoS)). The explosive growth and the advantages of bringing great convenience to economic management activities have prompted us to conduct a comprehensive analysis and explore the current challenges and opportunities facing the field of FinTech. It is essential for scholars who are interested in this field to conduct better and more in-depth research. Additionally, a comprehensive analysis helps investigate the development track characteristics and disclose statistical patterns through bibliometric analysis (Borgman and Furner 2002 ; Wang et al. 2018 ). Furthermore, this paper investigated the current research hot topics, identified the challenges, and predicted the future development trends.

Bibliometrics, as a statistical and quantitative analysis of academic literature, has access to visualizing the analysis results using science mapping analysis tools, such as CiteSpace and Vosviewer (Chen 2006 ; Stopar and Bartol 2019 ; Van Eck and Waltman 2010 ), thereby improving the readability of analysis results. Bibliometric analysis has been widely applied in different research areas, such as bitcoin (Merediz-Solà and Bariviera 2019 ), blockchain (Miau and Yang 2018 ), fuzzy decision making (Liu and Liao 2017 ), deep learning (Li et al. 2020 ), social sciences (Nasir et al. 2020 ), business and economics (Merigo et al. 2016 ), COVID-19 (Lou et al. 2020 ), financial innovation (Li and Xu 2021 ), poverty cycles (Qin et al. 2021 ), blockchain and cryptocurrency (Nasir et al. 2021 ), and journals ( European Journal of Operational Research (Laengle et al. 2017 ), Information Sciences (Yu et al. 2017 ), IEEE Transaction on Fuzzy Systems (Yu et al. 2018 ), Environmental Impact Assessment Review (Nita 2019 ), and International Journal of Systems Science (Wang et al. 2021 )). The two visualization tools, i.e., CiteSpace and Vosviewer, can assist the bibliometric method in revealing the static and dynamic characteristics of FinTech publications from various aspects. For example, the co-occurrence network of author keywords demonstrates the main research topics; the citation and co-citation analysis highlight the top influential objects; the burst detection analysis and timeline view can exhibit changes in a certain period. These processes are called science mapping analysis (Van Eck and Waltman 2010 ; Cobo et al. 2011 ).

The contributions of this paper can be summarized as follows: (1) Illustrate the basic features of FinTech publications, including the types, annual publications, main research directions by co-occurrence analysis of keywords, and dynamic changes of research focus by timeline analysis; (2) Explore popular countries/regions, institutions, journals, and authors and the collaboration relationship networks, and present the citation and co-citation networks to highlight the influential authors and journals; (3) Furthermore, detect the dynamic changes of cited authors, cited journals and cited references based on burst detection analysis, and more intuitively show the citation process of all FinTech publications based on overlay analysis; (4) With the current special environment, discuss the challenges FinTech faced and future possible development directions.

The rest of this paper is organized as follows: “ Data and methods ” section briefly describes the data and methods used in this paper. “ Fundamental characteristics of FinTech publications ” section presents the foundation characteristics of all FinTech publications, in terms of types, annual publications, current research directions and themes, co-occurrence, and timeline analysis of author keywords. The top productive countries/regions, institutions, and journals are presented in “ Productive object analysis and cooperation relationship analysis ” section. Additionally, the cooperation relationship is demonstrated. “ Citation structure analysis ” section investigates the citation structure, including citation and co-citation of authors and journals, respectively. Meanwhile, a burst detection analysis of cited journals, cited authors, and cited references is conducted. Furthermore, the current challenges and future possible research directions are discussed in “ Discussion ” section. “ Conclusions ” section ends this paper with some conclusions.

Data and methods

The literature data used in this paper are obtained from WoS (Falagas et al. 2008 ), one of the most widely used databases in academics, owned by Thomson Reuters Corporation. In this paper, we derived data through the search function in WoS by selecting as Database = Web of Science ™ Core Collection database; Topic search = FinTech or “Financial technology” or “Financial technologies”; Timespan = 1900–2020 (The data were derived on September 23, 2020. We searched the documents from the earliest time of WoS). As a result, 848 documents were retrieved and exported in plain text file format for software (CiteSpace and Vosviewer) bibliometric analysis. The contents in the derived documents are representative, including title, abstract, keywords, citations and references.

As presented in the Introduction, bibliometrics is used to highlight the development trajectory and characteristics of a particular research field (Mourao and Martinho 2020 ). This paper used the bibliometric analysis method to evaluate the development of FinTech documents from the following aspects: (1) start with the types and annual publications with significant indexes (such as numbers and rates), the distribution among different countries/regions, and important branches. Moreover, the productive countries/regions, institutions, journals, and authors are assessed by several recognized indicators, including the total number of publications (TP), the total number of citations (TC), the average citations per publications (AC), and H-index. (2) With statistics and visualization tools, the science mapping analysis is conducted to deeply master the characteristics of FinTech documents, such as the dynamic development trend. CiteSpace and Vosviewer, as two mature visualization tools, effectively illustrate the inner relationship of documents and visualize them in different ways, such as clustering and dynamic timeline (Chen 2006 ; Stopar and Bartol 2019 ; Kou et al. 2014 ). Through several bibliometric methods, including co-occurrence analysis, timeline analysis, burst detection and co-authorship analysis, this paper presented the keyword situation, citations, and cooperation networks of countries/regions and institutions on FinTech research. The whole process of bibliometric analysis in the field of FinTech can be illustrated in Fig.  1 .

figure 1

The research framework and process of this study

Fundamental characteristics of FinTech publications

Types and annual publications.

For the 848 obtained publications in the field of FinTech, the first document was written by Ronner and Trappeniers ( 1996 ), that is, Currency exposure management within Philips , a proceedings paper. The average number of publications is 33, which is low because a lot of literature has exploded in the past five years. The types of the 848 documents and annual publications are presented in Table 1 and Fig.  2 , respectively.

figure 2

The annual publications and citations

From Table 2 , most publications are articles with 531, accounting for 63.139%. Followed, 236 are proceedings papers, accounting for 28.062%; 51 (6.064%) are early access. Besides, there are 35 reviews, 33 editorial materials, 10 book reviews, four book chapters, two corrections, one data paper, and one meeting abstract. Articles and proceedings papers dominated the FinTech publications and accounted for 91.201%. Figure  1 illustrates the annual publications and the number of citations per year. 2019 has the greatest number of publications with 267 and ranks second for the numbers of citations with 1,061. 2020 and 2018 have 199 and 191 publications, respectively. 2020 has the greatest number of citations with 1,258. Furthermore, the H-index (Wang et al. 2020a , b ) of all documents is 27 and total citations are 3,338 (remove self-citations: 2,423). All the above phenomena reflect that FinTech is a relatively new field and has attracted wide attention recently. In contrast, it reflects that there is still room for development.

Figure  3 demonstrates the countries/regions with more than 20 publications. Taiwan, a part of China, is studied as a region in this paper. As shown in Fig.  3 , the United States is the most productive country with 159. Together with Taiwan, China ranks first with 187. Then, the third to the tenth are England (96), Australia (50), Russia (50), Indonesia (44), South Korea (44), Taiwan (41), Germany (40), and Switzerland (26), respectively. The top 10 countries/regions account for 82.076% of the 848 publications. The United States and China, as the top two most productive countries, have 305 publications, accounting for 35.967%.

figure 3

The countries/regions with more than 20 publications

Research directions and themes

The top 25 research directions of the publications are shown in Fig.  4 . Business economics and computer science are the most popular research directions. The number of publications for business economics is 408, and this accounts for 48.11%. The number of publications for computer science is 213, which accounts for 25.12%. Furthermore, research is widespread in government law (86), engineering (82), telecommunications (32), science technology and other topics (30), and environmental sciences ecology (29). FinTech covers many areas and promotes the development of numerous research directions. Based on Vosviewer, the visualization of the theme of the 848 publications is shown in Fig.  5 . We can see that, except for FinTech, some terms (e.g., data, market, model, system, bank, and neural network) have a high frequency. Moreover, Fig.  6 a presents the co-occurrence view of author keywords in this field. The co-occurrence method was first provided in the 1980s, which has been widely applied in bibliometrics or other fields and helps scholars grasp the study hotspots (Ding et al. 2001 ). We obtained 389 author keywords by setting the minimum number of occurrences of a keyword to 2 and merging financial technology and financial technologies into FinTech. The high-frequency keywords with the close co-occurrence relationship in the field of FinTech are shown in Fig.  6 b by setting the minimum number of occurrences of a keyword to 10.

figure 4

The top 25 hot research directions (generated using WoS on data)

figure 5

The theme of all 848 publications (generated using Vosviewer on data)

figure 6

The co-occurrence network of author keywords (generated using Vosviewer on data)

In Fig.  6 a, the nodes denote the author keywords, and the sizes mean the occurrences. The link between two keywords reflects that the two keywords appear in one paper simultaneously. The thickness of the lines between nodes means the number of co-occurrences of the two keywords. Specifically, the frequency of “FinTech” is 385 with 338 links (338 keywords appear with it) and 1,106 total link strength (a total of 1,106 times). “blockchain” has 98 occurrences and ranks second, followed by “financial inclusion” with 36, “innovation” with 36, “cryptocurrency” with 34, and “bitcoin” with 29. For “FinTech”, “blockchain” has the strongest connection with it (link strength is 57). Also, keywords that are closely related to “FinTech” include financial inclusion (link strength is 29), innovation (27), crowdfunding (22), and big data (18). In Fig.  6 b, we obtained 32 keywords. Popular topics include digitalization (the frequency is 21), machine learning (17), deep learning (11), and Internet finance (8).

Timeline view of author keywords

As shown in Fig.  6 , various keywords belong to different subareas. To understand the dynamic development trend further, this subsection illustrates their timeline view (Fig.  7 ). From this view, all keywords are classified into 5 clusters, i.e., P2P lending, finance law, lending, cryptocurrency, and technology acceptance model. Cluster 3 is the category with the longest time. The vast majority of keywords broke out after 2014. From 2014 to 2019, research topics, such as banking, mobile payment, P2P lending, financial regulation, e-payment, and big data emerged and continued. Digital economy, household finance, and financial stability were proposed in 2019. Mobile money appeared in 2020. The phenomena verify that FinTech is a new and hot field in recent years once again. With the rapid development of science and technology, much research will explode in a short time. Thus, it is necessary to summarize the research results on time, which highlights the significance of this paper.

figure 7

The timeline view of keywords (generated using CiteSpace on data)

Productive object analysis and cooperation relationship analysis

This section presents the most productive countries/regions, institutions, journals, and authors. Knowing their foci and situations help scholars locate authoritative academics and journals accurately. Moreover, as one important indicator, cooperation relationship analysis is another research point in this section.

The most productive countries/regions

Table 2 lists the top 5 most productive countries/regions. Some bibliometric indicators, such as TP, TC, AC, the number of publications that are cited equal to or more than 100/50 (≥ 100/ ≥ 50), and H-index are used to demonstrate the citation impact of the productive countries/regions.

The United States has the greatest number of citations with 1,235 and H-index with 18. The most cited US paper is the work of AN Berger The economic effects of technological progress: Evidence from the banking industry , a review, published in the Journal of Money Credit and Banking (its impact factor is 1.355, belongs to Q3). The paper examined technological progress and its effects on the banking industry (Berger 2003 ). China has 566 citations. The most cited Chinese paper is the work of KK Gai et al. A survey on FinTech , published in the Journal of Network and Computer Applications (its impact factor is 5.57, belongs to Q1). It produced a survey of FinTech by collecting and reviewing contemporary achievements and then proposed a theoretical data-driven FinTech framework (Gai et al. 2018a , b ). Table 3 lists the top 10 most frequent author keywords of the top 3 most productive countries (i.e., the United States, China, and England). Blockchain is one of the most common research topics, and the frequencies for each country are 13, 17, and 7, respectively. Furthermore, scholars in the United States and China pay more attention to P2P lending, financial inclusion, and cryptocurrency. Regulatory technology (RegTech) is another key topic for Chinese and British scholars. In summary, current hot research directions include blockchain, P2P lending, big data, financial inclusion, and regulation.

The cooperation relationship among countries/regions is illustrated in Fig.  8 . There are a total of 86 countries/regions, and we selected 62 terms by setting the minimum number of documents of a country to 2 and presented the closest cooperation network, including 52 terms. They are divided into 11 clusters, and different colors represent different categories. In Fig.  8 , the nodes represent the countries/regions, and the sizes of the nodes denote the number of documents. The links between two nodes denote that they have a cooperative relationship with each other. The thicker the link is, the greater their collaboration. The most cooperative countries/regions in each category are the United States, China, England, Russia, Australia, Canada, and South Korea, respectively. The United States is the most cooperative and often cooperates with China, which can be reflected by the links between them. From the perspective of TP and TC of the United States and China, the importance of cooperation is clear.

figure 8

The collaboration network of countries/regions (generated using Vosviewer on data)

Productive institutions

Table 4 exhibits the top 5 most productive institutions. In the list, the University of London has the greatest number of TP, and it is the only institution that has published more than 50 citations. The Ministry of Education Science of Ukraine, University of New South Wales Sydney, University of Hong Kong, and Massachusetts Institute of Technology follow. In terms of TC, the University of New South Wales Sydney ranks first with 148. The second to the fifth institutions are the University of London (111), University of Hong Kong (77), Massachusetts Institute of Technology (72), and Ministry of Education Science of Ukraine (6). The University of New South Wales Sydney and the University of Hong Kong jointly published an article, FinTech, RegTech, and the Reconceptualization of Financial Regulation (Arner et al. 2017 ). It is the most cited publication in the field of FinTech. The phenomenon drives us to explore the cooperative relationship among institutions.

There are 1,048 institutions in the field of FinTech. Figure  9 presents the cooperative relationship networks of all institutions and the closest network of 333 institutions. In Fig.  9 , the sizes of the nodes present the number of documents. The gray nodes indicate that the articles published by these institutions in the field of FinTech were not done in cooperation with other institutions. To improve the TP and TC of publications in this area, cooperation urgently needs to enhance. In the closest cooperation network, the size of the node represents the number of total link strengths. As a result, Singapore Management University is the most cooperative institution, and its total link strength is 38 related to 8 documents with 81 citations. The University of Minnesota System (its total link strength, related documents, and citations are 32, 6, and 81, respectively), New York University (32, 6, and 77), City University of Hong Kong (32, 7, and 56), and the City University of New York (30, 4, and 61) follow. Even though they are not in the top 5 most productive institutions list, their influence is at a higher level.

figure 9

The cooperation networks of institutions (generated using Vosviewer on data)

Productive journals

Table 5 lists the top 10 most productive journals and their corresponding important indicators, such as TP, TC, and impact factor (IF), ≥ 100 and ≥ 50. Considering that FinTech is a new field, the number of TP and TC is relatively low. For the top 10 list, three are proceedings papers/books. Four belong to Q1 journals, including Electronic Commerce Research and Applications , Journal of Management Information Systems , Financial Innovation and IEEE Access . Additionally, 5 journals have IF greater than 2. Electronic Commerce Research and Applications (its TP is 15) is the most popular journal for scholars in the field of FinTech, and it has the greatest number of TC with 248. Financial Innovation , as a new journal launched recently, has 8 publications related to the keywords FinTech. It has a relatively high level of TC with 80.

Moreover, only one journal, i.e., Electronic Commerce Research and Applications , has published a paper cited more than 100. The paper is, The economics of mobile payments: Understanding stakeholder issues for an emerging financial technology application . It examined a new technology application, in association with the revolution in wireless connectivity, i.e., mobile payments (Au and Kauffman 2008 ). Similarly, the number of articles posted by authors is scattered. The authors who have published more than 5 papers are DW Arner, RP Buckley, RJ Kauffman, SH Huang, D Wojcik, and J Zhang. Among them, the work of RJ Kauffman has the most citation with 151, i.e., The economics of mobile payments: Understanding stakeholder issues for an emerging financial technology application . FinTech, RegTech, and the Reconceptualization of Financial Regulation (Arner et al. 2017 ), the work of DW Arner and RP Buckley has the most citation (38) for the documents published by them in the field of FinTech, which once again confirms the importance of cooperation. The cooperation network is shown in Fig.  10 . Setting the threshold to 2 means the minimum number of documents of an author. We obtained the closest network (Fig.  9 a) from the complete cooperation network (Fig.  10 b). According to the above analysis, the spirit of cooperation is worth promoting to enhance the influence of publications and authors.

figure 10

The cooperation network of authors (generated using Vosviewer on data)

Citation structure analysis

To investigate the influence of the cited authors and journals further, this section conducts citation structure analysis, including citation analysis and co-citation analysis in terms of authors and journals. Thus, scholars who are interested in this field can locate authoritative academics and journals accurately.

Citation explains the number of times the author has been cited, and co-citation reflects that two authors/journals/references/sources are cited in one paper simutaneously. According to Vosviewer, we obtained 138 authors out of 2,025 based on the minimum number of documents of an author (i.e., the threshold is 2), and 90 cited authors out of 20,877 based on the minimum number of citations of an author (i.e., the threshold is 20). The citation network and co-citation network are presented in Fig.  11 . 79 of 138 cited authors constitute the closest citation network. They are divided into 7 clusters. The nodes and their sizes denote the authors and the citation degree, respectively. The greater the node, the more times the author is cited. 89 of the 90 cited authors constitute the closest co-citation network and are divided into 6 clusters. The connection between the two cited authors indicates that they appeared in one paper. The thicker the line, the more frequently the two authors appeared together.

figure 11

The citation and co-citation network of authors (generated using Vosviewer on data)

For more details, Tables 6 and 7 presents the top 10 most cited authors and most co-cited authors based on some important indicators, including TP, TC, links (the number of the authors cited together), the total link strength (the weights of links), and cluster.

The most cited paper with 158 citations is not included in Table 6 because AN Berger has only published one document in the field of FinTech. According to the results, among the authors who have published more than 2 papers, RJ Kauffman is the most influential with 6 publications and 675 citations, which is obvious in Fig.  11 a. Kauffman focused on researching the FinTech revolution including mobile payment and cards, evaluating changes and transformations in different areas of financial services (Au and Kauffman 2008 ; Gomber et al. 2018 ; Kauffman et al. 2017 ). Additionally, DW Arner is the most co-cited author in the co-citation network (the number of citations is 104). 79 authors have been cited with him. Arner focuses on digital financial service and its regulation, sustainability features, and RegTech (Arner et al 2017 , 2020 ; Zhou et al. 2015 ). Furthermore, the citation network and co-citation network of journals are illustrated. The corresponding indexes are listed in Table 8 . The meanings of the nodes, sizes of the nodes, the links, and their thickness are similar to that of the authors.

From Fig.  12 , the top cited journals are Electronic Commerce Research and Applications , Journal of Money Credit and Banking , Social Studies of Science , Accounting Organizations and Society , Journal of Economics and Business , Business Horizons , and Financial Innovation . The top co-cited journals include Journal of Finance , Management Science , MIS Quarterly , Review of Financial Studies , Journal of Financial Economics , Journal of Banking & Finance , Electronic Commerce Research and Applications , American Economic Review , and Strategic Management Journal . Next, combining the top citation journals with the top prolific journals (Table 5 ), we selected three important journals belonging to Q1 ( Electronic Commerce Research and Applications , Journal of Management Information Systems , and Financial Innovation ) to represent their research focus and help scholars conduct targeted research based on the co-occurrence analysis of author keywords, as shown in Fig.  13 . We obtained 84, 50, and 33 author keywords of the three journals, respectively. Except for the general keyword, i.e., FinTech, the foci of Electronic Commerce Research and Applications include cryptocurrency, blockchain, digital economy, mobile payment, and bitcoin. The top hot topics of the Journal of Management Information Systems are business models, crowdfunding, P2P lending and bitcoin. For Financial Innovation , big data, blockchain, and digital banking are popular research topics. Combining with Fig.  5 and Table 3 , the main research subfields are obvious.

figure 12

The citation and co-citation network of journals (generated using Vosviewer on data)

figure 13

The research topics of top important journals (generated using Vosviewer on data)

Then, this paper conducted a the burst detection analysis (a popular method that can reflect the explosive data attracted attention by the academic in a certain period) (Xu et al. 2019 ; Kleinberg 2003 ) for the cited authors, journals, and references. It can be used to reflect the dynamic changes of publications in the field of FinTech.

The visualization of the cited authors is shown in Fig.  14 . The node and its size denote the cited authors and citation, respectively. The red nodes represent the authors with the strongest citation bursts. As a result, we obtained 6 terms with the strongest citation bursts; one of them is the name of a forum, the World Economic Forum. Thus, Table 8 lists the other 5 cited authors with the strongest citation bursts.

figure 14

The visualization of the cited authors (generated using CiteSpace on data)

From Table 8 , we have noticed that most of the research was done recently. Not surprisingly, only a few authors have the strongest citation bursts and are all close to the present. I Lee, G Buchak, and T Beck have been closed to 2021. Of the 848 publications, I Lee published only one paper, FinTech: Ecosystem, business models, investment decisions, and challenges . It introduced a historical view of FinTech; discussed the ecosystem of the FinTech sector, various FinTech business models, and investment types; and illustrated real options for FinTech investment decisions (Lee and Shin 2018 ). The paper, FinTech, regulatory arbitrage, and the rise of shadow banks , published by G Buchak, studied the contributions of regulatory differences and technological advantages to the growth of the shadow bank market (Buchak et al. 2018 ). FinTech has widespread applications in various fields and is still expanding.

According to CiteSpace, 14 journals have been cited frequently in a certain period (Table 9 ). The citation frequency for most of the journals has increased since 2016. The strength of the Financial Times is the strongest with a value of 8.2555. Financial Times is a newspaper edited in London and has a strong influence on the financial policies of the British government. For academic journals, the strength of the Harvard Business Review is the strongest with 5.2399. It focuses on leadership, organizational change, negotiation, strategy, operations, marketing, finance, and managing people. The citation burst of the cited journal of Economic and Social Review has the longest duration of 12 years from 2006 to 2017. Moreover, only one has continued until 2021, i.e., Business Horizons .

Next, we studied the relationship between references related to FinTech. Based on CiteSpace, the reference network is constructed as shown in Fig.  15 . Similarly, the red node is the reference with the strongest citation burst. There is only one reference with the strongest citation burst (its strength is 3.6071), i.e., Social media analytics for enterprise: typology, methods and processes . It provided an overview of social media analytics for managers (Lee 2018 ), and was published on Business Horizons in 2018. As listed in Table 10 , the duration result begins in 2019 and continues to 2021, which reflects that its influence is continuing in the field of FinTech.

figure 15

The visualization of the reference network (generated using CiteSpace on data)

To show the trends of the documents more intuitively, Fig.  16 presents the overlay analysis (Nita 2019 ). It is divided into two parts, i.e., citing (left part) and cited (right part). The curves denote the citation connections. For the oval in Fig.  15 , the horizontal axis and the vertical axis reflect the numbers of authors and documents, respectively. The more papers published in journals in specific fields, the longer the vertical axis. The greater the number of authors, the longer the horizontal axis. On the left, we can see that the journals mainly belong to cluster 1 (mathematics, systems, mathematical), cluster 6 (psychology, education, health) and cluster 10 (economics, economic, political). Correspondingly, the number of authors is large. For the cited part, the references are involved in many areas, for instance, chemistry, materials, physics (cluster 4), environment, toxicology, nutrition (cluster 2), molecular, biology and genetics (cluster 8). Of course, they are mainly concentrated in the same three areas as the left. In comparison, the research in the field of FinTech has a widespread impact on many fields and is still expanding.

figure 16

Overlay analysis of all 848 publications (generated using CiteSpace on data)

FinTech is still at an early stage. Combining with the theme view, co-occurrence networks, and a timeline view of author keywords, the related research areas mainly involve lending, blockchain, machine learning, big data, financial regulation, and financial inclusion. The research not only promotes scientific and technological progress but also plays a key role in economic development. To clarify the challenges and possible opportunities reasonably in the future, this section combines the derived documents and the characteristics of the current economic environment. Subsequently, the challenges and development prospects are discussed, especially from the perspective of the impact of big data and COVID-19 on FinTech.

As a critical technology and one of the research topics closely related to FinTech (see Fig.  6 ), big data has a great influence on reshaping the market by introducing new algorithmic technologies and is the key production element of the digital economy and digitalization (Gruin 2020 ). It is the best technical support for financial innovation. The integration of technologies such as big data and cloud computing has promoted the rapid development of the Internet of Things, which has realized the interconnection and intercommunication of people, people and things, and things and things, leading to explosive growth in the amount of data.

As the core means of production and production factors of Digital Economy 2.0, the value of data needs to be realized by the technology cluster of the supporting layer, including artificial intelligence, blockchain, and AR/VR. Data intelligence is the core of future financial (Gai et al. 2018a , b ).

With the development of the social economy, progressively more financial companies are beginning to build their big data platforms, from banks to P2P to insurance and securities. Ensuring data security and improving data usage efficiency, distinguishing and filtering the interfering elements, and obtaining more effective models or financial products will play a vital role (Hung et al. 2020 ). For example, (1) banking will analyze behavior data of clients, including deposits, withdrawals, and electronic transfers, and then conduct marketing, financial product innovation, and satisfaction degree survey to send the targeted advertising information; (2) for machine learning, good data help improve the capability to predict future situations based on known variables in the learning process (Yeh and Chen 2020 ). The important content of big data security and a simple big data platform is shown in Fig.  17 . Footnote 1

figure 17

Big data security

Influence of COVID-19 on FinTech

For various fields of research, it is important to identify the intrinsic features of complex data and use them, not limited to financial big data (Huang et al. 2021 ). At present, because of the impact of the pandemic, FinTech products and services face many uncertainties and unpredictable risks because many banks and financial institutions had offered online loan application services based on remote data during the COVID-19 pandemic. Najaf et al. ( 2021 ) proposed that the COVID-19 had brought a drastic change in the key determinants of P2P lending. Chen et al. ( 2021 ) investigated the impact of FinTech products on commercial banks’ performance in China. The impact of COVID-19 on financial constraints and the moderating effect of financial technology are examined by Ling et al. ( 2021 ). The development of FinTech can alleviate the negative impact of COVID-19 on corporate financial constraints.

On one hand, the explosion of the pandemic encourages the company to review its products and progress. The importance of financial regulation is self-evident (Yi et al. 2020 ). On the other hand, enterprises and banks should expand current products and create new service lines to accelerate the transition to e-commerce. The transition pushes enterprises and banks to reexamine and reconstruct the digital strategies aimed to master new opportunities and digital customers.

Taking lending as an example, because of online operations, it is more difficult to obtain complete client information compared with traditional face-to-face work, which can result in many malicious fraudulent loans. This will not only affect the development of enterprises but also cause a decline in the post-loan management ability. Furthermore, from the top 5 most cited authors with the strongest citation bursts by 2021, Buchak was ranked second with research on how two forces, regulatory differences and technological advantages, work online. From the top 10 most cited authors, Arner was ranked second and presented that the financial system requires increasing the use and reliance on RegTech (Arner et al. 2017 ). We confirmed the importance of financial regulation in the field of FinTech. Additionally, here is a challenge for financial regulators to achieve network security and decrease current online microfinance. That is, from the perspective of financial risk, how to effectively use technology, improve supervision tools, and optimize supervision paths is another challenge.

Research feature and development prospects

The research hotspots in the field of FinTech mainly focus on specific technologies in practice and emphasize the role of the finance field. This causes insufficient theoretical discussions and neglects the innovation of technology itself. FinTech is the integration of finance and technology, the latter pushes the development of the former. Thus, in the next stage, how to jointly promote the innovation of finance and technology and achieve deep combination is the third challenge proposed in this paper.

However, with the advancement of big data, cloud computing, artificial computing, and blockchain, FinTech still has many opportunities and broad prospects for development. The core is to introduce new elements and combine them with multiple disciplines to promote the technology level. Specifically, (1) improving the modern regulation system and highlighting risk management methods help accelerate the realization of effective financial regulation and promote the stable operation of financial institutions legally. For example, under the mobile payment and artificial intelligence environment, ensuring consumer safety and avoiding information leakage is the main task of enterprises and banks at present and in the future (Tritto et al. 2020 ); (2) as analyzed above, the transformation triggered by emerging technologies in the FinTech has been mainly manifested in the technology applications; however, it should dig deeper into more basic theories (Mao et al. 2019 ); (3) with the assistance of artificial intelligence and machine learning, studying the predictive procedures with high accuracy, stability and robustness will benefit the financial market, like predicting capital markets (Alam et al. 2020 ) and the stock market. Additionally, the corresponding fuzzy decision-making theories and methods are helpful (Liang et al. 2017 ; Xu and Wang 2016 ). Similarly, strengthening further integration with big data will make it easier to quantify subjective and objective indicators, such as sentiment indicators.

Conclusions

In this paper, we have presented an overall analysis of publications in the field of FinTech up to 2020. Based on WoS, we obtained 848 publications; the first document was published in 1996. Even though this topic appeared early, with the advancement of the economy and technology, the real explosions of research occurred in 2015 (see Fig.  2 ). From various aspects, this paper investigated the characteristics of all publications in the field of FinTech based on visualization tools.

First, the development of FinTech benefits from common progress in many fields, such as blockchain, big data, machine learning, artificial intelligence, and digital economy (see Fig.  6 ). Moreover, mobile money is currently a hot topic and will continue to be one (see Fig.  7 ). In light of countries/regions, China has the greatest number of publications, which can be illustrated by the advanced technological environment of China, such as the convenient mobile payment and intelligent life. Meanwhile, it is expected to achieve the progress of citations (see Fig.  3 and Table  2 ). The research hotspots of this field are clear. It can be reflected from different angles: (1) from the top productive countries/regions (see Table  3 ), their research topics concentrate on the blockchain, P2P lending, financial inclusion and regulation; (2) from the popular journals (see Fig.  13 ), Electronic Commerce Research and Applications , Journal of Management Information Systems , and Financial Innovation are the top influential journals. Their research focuses on e-commerce, digital economy, blockchain, big data, and banking; (3) from the timeline view of author keywords (see Fig.  7 ), and it not only presents the popular topics but also illustrates their time. Furthermore, burst detection analysis and overlay analysis give impetus to the scholars who are interested in FinTech to grasp the dynamic changes more intuitively. With the overall results, the current challenges (lending, risk management, and financial regulators) and future possible research directions and extensions (introducing uncertain decision making and speeding up the connection with machine learning and big data) are discussed.

In general, the findings in this paper play a key role in the next stage and encourage scholars to conduct further studies. However, because the publications presented in this paper are limited to the WoS score database and the search keywords are related to FinTech and financial technology, the content needs to be enriched in the future. We will pay more attention to the innovation research of FinTech and its dynamic development.

Availability of data and materials

Data used in this paper were collected from Web of Science Core Collection.

https://www.sohu.com/a/163585024_621613

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Emerging advances of blockchain technology in finance: a content analysis

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  • Rashikala Weerawarna 1 ,
  • Shah J. Miah 1 &
  • Xuefeng Shao   ORCID: orcid.org/0000-0002-4267-9600 1  

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Blockchain has become a widely used information system technology recently because of its effectiveness as an intermediary-free platform. While the use of blockchain in various fields, such as finance, supply chains, healthcare, education, and energy consumption, is increasingly enabling the development of Internet-enabled “distributed databases,” there are not many exploratory studies available to provide an understanding of how the field is progressing. Therefore, it is imperative to explore the status quo of blockchain technology in the finance sector, particularly highlighting how blockchain architectures can aid the finance sector to gain competitive advantage. This systematic literature review analyzes the content of the 50 most relevant articles and professional industry reports through peer-reviewed relevant academic literature in the finance sector from 2008 to 2022 to identify several possible features of blockchain research in the financial sector. This study highlighted the dimensions of blockchain technology, blockchain in finance, its competitive advantages, the current status of finance, and various challenges that keep the implementation of blockchain-based financial information systems at the initial stage. We identified three main areas that require research attention in order for blockchain technology to become the “next-generation networks” that will revolutionize the financial sector.

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1 Introduction

As a revolutionary technology invention after the Internet [ 1 , 2 ], blockchain enables new online businesses to acquire and gain the trust of stakeholders for data transactions. It has become a common technology that has transformed the status quo of many business functions, such as finance, healthcare, supply chains, education, and energy consumption. This technology guarantees secure, immutable, decentralized, and transparent data services, enabling quick transactions at low cost for various business network stakeholders [ 3 ]. Central banks, financial institutions, and technological firms are all interested in blockchain technology as the financial sector has understood its huge potential [ 4 ]. This implies that this innovation would create a significant push to transform the structure of financial services, how the entire financial industry operates, and reinvents the banking industry [ 5 , 6 ] and address dynamic changes while providing effective data solutions that meet the demands of modern distributed industries. For instance, by implementing blockchain technology, traditional banking systems can gain competitive advantages in securing transactions at a lower cost [ 7 , 8 ]. Existing studies have shown that banks are continuously exploring options for implementing blockchain [ 9 , 10 , 11 ]. The Interbank Information Network, which is the largest blockchain payment network (Global partners: J. P. Morgan, Australia-New Zealand Banking Group Ltd., and Royal Bank of Canada), has already started its journey with blockchain technology applications [ 10 , 11 ]. A frequently used application is the American Express instant blockchain-based payment system, which was developed on the Ripple platform. Footnote 1 Because of the rapidly growing use of the Ripple network, remittance companies such as MoneyGram have also joined Ripple to overcome the barriers of cross-border transactions and concerns with legal compliance.

The use of the Internet, the latest mobile applications, and other smart mobile devices reinforced the demand [ 12 ] for features and services that the finance sector may offer for effective electronic transaction processes and provisions in financial information systems. FinTech, the combination of technology and financial services [13, p.3; 14], has become a well-discussed area of study in the new era of the business industry. FinTech companies offer many services, such as digital cash, cognitive systems, and distributed ledger technology. Traditional financial organizations and start-up companies are increasingly partnering with FinTech [ 15 ] to provide user-friendly and cost-effective financial electronic services. Blockchain is a common FinTech that transforms how financial businesses operate, collaborate, and transact with their stakeholders [ 16 ]. This suggests that the decentralized financial network is bringing about a major financial sector revolution with the enhancement of digital wallets as a crucial component of financial inclusion. The decentralized electronic ledger system will transform the way transactions are carried out, changing the shape and size of the financial sector. Studies [ 1 , 17 ] have demonstrated that research on blockchain applications can save business money and open up new commercial and export opportunities, both of which will expand in the near future. According to the Australian Blockchain Roadmap [ 18 ], Gartner predicts that “blockchain will generate an annual business value of over US$175 billion by 2025 and over US$3 trillion by 2030. By 2023, blockchain will support the global movement and tracking of US$2 trillion worth of goods and services annually.” This shows the rapid growth of blockchain implementation in the commercial and financial service sectors.

Blockchain technology has emerged as a revolutionary and disruptive [ 19 , 20 , 21 , 22 ] innovation in both technology and economics in the finance industry and requires a critical level of data integrity. The main objectives are to replace the existing process by eliminating the need for the “trusted third parties.” Undoubtedly, in the finance sector, the global money remittance and automated banking industry will be disrupted by blockchain transformation. PricewaterhouseCoopers (PwC) in its 2019 Global FinTech Report stated that “FinTech is a major disruptor in the financial services industry” and it affects “the way financial services market players do business.” To secure the integrity of financial systems and prevent money laundering, it is vital for financial big data Footnote 2 to be validated and verified to offer credibility. The use of blockchain with security encryption and smart contracts in the finance sector enhances identity management, transparency, trust, and privacy [ 16 ]. Research supports the evidence that blockchain technology can provide efficient, fast, and low-cost transaction platforms.

Although blockchain transforms the current forms of FinTech to gain a competitive advantage, it still has vulnerabilities, drawbacks, and enormous challenges to overcome. Gan et al. [ 21 ] investigated the aspects of designing FinTech: technological, organizational, usability, social, and regulatory. In an IEEE editorial note, Choo et al. [ 23 ] summarized the technological and management opportunities and challenges in a blockchain ecosystem. The involvement of several stakeholders increases the complexity of blockchain usage in the finance sector. The complexities vary in different aspects, such as technical, capability, knowledge, experience niche, regulatory, and cybersecurity. Blockchain and its applications require regulatory frameworks suitable for their intended purpose similar to any other disruptive new technology. This is the time to expand the awareness of the disruption and long-term benefits and enhance the knowledge to assist with this transformation. Researchers [ 9 , 10 , 11 ] have investigated the challenges that the financial sector faces when implementing blockchain technology. They have identified many challenges, such as maintaining trust, ensuring the security of blockchain systems, ensuring the integrity of data, identifying participants in blockchain systems, balancing privacy with transparency, tech-neutrality, secure interpretability, and the legal status of smart contracts. It is difficult to guarantee trust and anonymity in platform-mediated networks.

The major disadvantage of blockchain is scalability, which relies on throughput [ 2 , 3 ]. It requires a high throughput while dealing with a large volume of transactions. For example, blockchain has a very low throughput compared to Visa and PayPal (Fig.  1 ). In some cases, the immutability of the blockchain itself becomes an issue in ensuring effective transactions. There are gaps in the state-of-the-art literature relevant to the theoretical, technological, practical, and social aspects of blockchain technology.

figure 1

Transaction speed

This literature review was prompted by an investigation of how blockchain technology revolutionized the finance sector to become its future, given that relevant financial sector research is still in its initial stages compared to blockchain technology research in other disciplines. Blockchain-based information systems are significant for financial institutions, particularly remittance companies and banks, where identity management and transparency are crucial to their financial transactions. Ensuring the security and reliability of big financial data is also crucial. Blockchain-based financial applications will be advantageous to banks, remittance companies, audit firms, financial trading companies, digital asset management companies, and any type of financial and e-finance institution.

This study aims to highlight how blockchain architecture can help the finance sector achieve a competitive advantage. We describe the features of blockchain technology and how they help resolve issues in the financial sector. We highlight the recent growth in research interest in blockchain technology in the financial sector and categorize its study fields. Exploring this topic, the focus of this study aims to recognize the potential of blockchain in the finance sector and develop the knowledge of its core components, major challenges, and current research gaps.

The rest of this paper is structured as follows. In Section  2 , we describe the fundamentals of blockchain technology, the blockchain revolution, and blockchain applications in the financial sector. In Section  3 , we explain the systematic literature review methodology of this study and develop the research questions. In Section  4 , we provide an overview of the existing work, present the findings, and discuss the problems to be solved. In Section 5 , we outline the conclusions and possible future research opportunities.

2 Study background

2.1 basics of blockchain technologies.

The first and most fundamental blockchain technology was introduced in November 2008 as an electronic cash system, the distributed ledger behind Bitcoin transactions, by Satoshi Nakamoto (an unknown programmer or group of programmers) [ 24 ]. This system was viewed as a paradigm shift, given that it comprises four core components as shown in Fig.  2 : hash, digital signature, peer-to-peer network, and consensus mechanisms. The technology used in distributed digital ledger record transactions as an immutable list of blocks that are shared in a peer-to-peer network. It does not require a single server, thus eliminating a single point of failure. Each piece of information in the block is encrypted, and each block is linked to the previous block using a unique identifier created using the hashing algorithm [ 25 ]. A new block of data can be appended to the ledger only if majority of the nodes agree that it is valid. The entire process of transaction verification and addition to the blockchain is called mining, which requires a highly configured hardware computational power. The agreement between multiple nodes (participants) about the validity of a block is derived via a consensus algorithm, such as proof-of-stake (PoS), proof-of-work (PoW), proof-of-elapsed time (PoET), delegated proof-of-stake (DPoS), and practical byzantine fault tolerance (PBFT). Each block in the blockchain encrypts the transaction using a cryptographic hashing function (Hash256), which provides security in blockchain technology. All these components enhance the attributes of the blockchain: security, trust, privacy, and anonymity [ 25 ].

figure 2

Four core components of the blockchain technology

2.2 Revolution of blockchain technology

Blockchain has been revolutionized in four stages since 2008 (Fig.  3 ); the first of which is cryptocurrencies, particularly Bitcoin. The popularity of blockchain has increased because of the introduction of Bitcoin. The second era introduced monetary transactions and smart contracts in mortgages, loans, and other monitory bonds, an automated computer program that executes automatically. In the third era, it has increased in digital society by enhancing the features of smart contracts. The fourth era concerns industrial decentralized ledger systems in different industries such as government, healthcare, supply management, education, energy, and finance [ 12 ]. Blockchain technology has advanced from being a platform for digital currencies to smart contracts to decentralized applications (DApp) with high-speed and expandable decentralized storage and decentralized communication to the infrastructure available for Industry 4.0.

figure 3

Blockchain evolution

2.3 Blockchain in the financial sector

In the financial sector, interest in blockchain has grown. The use of blockchain technology in finance to conduct money transfers, cross-border payments, identity confirmation, contractual agreements, trade finance, insurance, smart contracts, auctions, and currency trading has led to its exponential growth. Western countries (for example, the USA, Australia, Canada, South Korea, Russia, and Israel) have been encouraged to invest in blockchain-oriented application development [ 26 ]. The three main blockchain applications (Fig.  4 ) in finance that have been identified include cryptocurrencies and their trading platforms, digital asset registers and management, and cross-border payments. According to Deloitte [ 27 ], blockchain applications can be used to transform finance processes: “intercompany transactions, procure-to-pay, order-to-cash, rebates, warranties, and trade financing.”

figure 4

Blockchain applications

The primary factor in financial transactions that contributes to the success of a financial institution and the effective use of an application is trust [ 27 ]. Blockchain establishes trust, instructs the Internet on how to transfer money or other assets via secured smart networks using a cryptographic algorithm, and guarantees that the money is spent only once. The current legal channels used in remittances are banking services, automated teller machine (ATM) withdrawals abroad, money transfer operators (MTOs), cash transfers (through informal couriers), and carrying cash when returning home (Kasiyanto, 2016). The current challenges of cross-border remittances are data collection and verification as well as transaction costs. Lowering the transaction costs of remittances is not an easy task. Blockchain technology plays a major role in finance [ 28 ].

The traditional and labor-intensive identification method used by FinTech institutions to go through the know your customer (KYC) process [ 25 ] increased the overhead of the business. Blockchain-oriented systems can overcome business overheads in financial institutions by providing efficient KYC processes [ 8 ]. Evidence is also reported in the government sector. For example, in March 2015, the UK government set out its approach to digital cryptocurrencies by adopting a friendlier regulatory stance. Furthermore, the UK government plans to invest money in the potential future of leveraging blockchain technology to fundamentally change the financial world [ 4 ]. Although there is a high demand for blockchain technology in the finance sector, it is still in its infancy.

3 Systematic literature review methodology

A systematic literature review was conducted in three phases (Fig.  5 illustrates the details of the phases) using the existing peer-reviewed literature in the finance sector from 2008 to 2022. Our scope includes finance research and covers blockchain technology studies. In the initial phase, we identified the scope of the research area, focusing on blockchain technology in the finance sector. The timeframe for the study was obtained from the first reference to the blockchain published by Satoshi Nakamoto in 2008. To ensure credibility, only peer-reviewed articles were selected from the eight different databases. Phase two was conducted using two main strategies. First, the review concentrated on the “Abstract” section of articles found relevant to the sector. We then moved the review to a deeper content analysis, where we needed to gain extant knowledge and understand the intellectual structure of blockchain in the finance sector. The final phase began with an evaluation of the gathered information to determine the contributions to the topic and then we synthesized the results into a summary of known and unknown findings and controversy. Finally, the research questions were formulated, and further research areas were identified.

figure 5

Phases of the literature review

Our primary objective is to gain insights into blockchain technology in the finance sector, particularly highlighting how the blockchain architecture can help the finance sector achieve a competitive advantage. As illustrated in Fig.  6 , we referred to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to systematically locate, select, and evaluate article samples. The 50 articles reviewed in this study were sourced from 26 journals in eight different databases related to business, finance, and information systems ( ACM Digital Library , Directory of Open Access Journals (DOAJ), SAGE journals , ScienceDirect , ProQuest , EBSCOhost , Taylor & Francis Online , and SpringerLink ). Sample articles were identified to understand the topic and its influence. The thematic content analysis methodology has been used to systematically identify significant research topics, potential business benefits, and potential challenges of applying blockchain technology to the finance sector.

figure 6

Sample collection and analysis process

By analyzing other similar literature review articles, we adopted a stepwise approach [ 29 , 30 ] to develop our research questions. Research questions can be developed using different formats depending on the aspect to be developed, such as existence, description and classification, composition, relationship, and descriptive [ 29 ]. The literature reviewed in this study framed the research questions based on the existence of a particular theory revealing an explanation, description, and classification where the questions show uniqueness and relationships that evaluate the relationship between variables.

In the development process of research questions, as many researchers have done, we began identifying the broader subject of interest in blockchain in the finance sector. We framed the questions based on the composition, where the research was broken down into components. In our preliminary research, we identified the existing information that clarifies information gaps in the blockchain in the finance sector. We then discuss what we should still know. Every research question in our study leads to a more specific question: “Is blockchain technology the future of the finance sector?” Therefore, the following questions were developed to narrow the scope and focus of this study.

This study intends to answer the following research questions to identify blockchain as the future of the financial sector.

How has blockchain technology been used in the finance sector since its emergence?

Can blockchain technology streamline different financial services, including payments, interbank processes, international remittances, and financial accounting [ 4 , 26 , 31 , 32 ]?

Has blockchain technology disrupted the financial sector? Blockchain technology can disrupt asset management payments, compliance, and insurance processes in the financial sector [ 1 , 15 ].

Why is it still in its initial stages? The research trend is widespread in terms of business opportunities, challenges, and regulatory requirements [ 2 , 6 , 32 , 33 , 34 , 35 ].

4 Findings and discussions

This review investigated the blockchain literature to provide useful research insights in the financial sector to assess its technical value by adding and addressing issues relevant to application design aspects. Our study identified major obstacles to realizing ubiquitous smart applications as well as an important aspect for generating new conceptual knowledge in the problem domain. Previous studies have attempted to investigate blockchain technology, its challenges, and its applications in finance rather than focusing on blockchain in the banking, finance, and FinTech categories in depth.

To the best of our knowledge, no reviews have either explored the adequate theoretical grounding and empirical rigor or identified the areas that need to be addressed in a single research review. This is despite earlier studies on blockchain in finance showing room for improvement in various ways. In contrast to previous work, our investigation addresses a broader view of blockchain in finance to justify the statement “The future of finance is blockchain” by revealing the current status of the financial sector and how blockchain can revolutionize it. We recognized the technology evolution, its disruptions, and blockchain implementation challenges. Our analysis identified three major foci of blockchain implementation in the finance sector: business, technology, and social aspects. We argue that successful implementation of blockchain in finance is possible if these areas are addressed appropriately. The contribution of this study is that it assists researchers and practitioners in making a strategic view of implementing blockchain in the finance sector.

The following sections include details that summarize our findings.

4.1 Blockchain disruption and the future of finance

Blockchain is an innovative technology that disrupts the financial sector and transforms the financial sector, and its use in FinTech is surging in popularity. FinTech can be classified in two ways. First, financial institutions embrace technology to advance their operations. Second, technology companies use their technology to develop financial technological services [ 6 ]. FinTech has evolved in three phases. The first phase is mobile payment, such as Apple Pay. The second phase is smart contracts, such as DocuSign. The third phase is blockchain. Although blockchain is still in its initial stages, the industry has sensed the pulse of this transformation and is eagerly waiting to understand the architecture, design, implementation, and maintenance of this technology. The lack of knowledge, understanding, cooperation, and communication among stakeholders creates barriers to this transformation.

4.2 Blockchain in finance research interests

The number of publications on blockchain technology in finance has recently increased. Although blockchain finance has not shown any interest since 2008, it has gradually gained attention and reached its peak in 2018, as shown in Fig.  7 . The articles used in this research support the increased interest in blockchain finance since 2015 (Fig.  8 ). Banks and other financial institutions have realized that blockchain technology can optimize the finance sector. Furthermore, to be sustained in the industry, they must understand blockchain technology and embrace it sooner than later [ 2 ].

figure 7

Google trend search volume—blockchain in finance January 2008–2022

figure 8

Publication article trend 2008–2022

Blockchain technology has gained interest in many fields that require high performance, security, transparency, and cost efficiency since Nakamoto published his work on Bitcoin cryptocurrency. In the IEEE special issue [ 23 ], it is stated that 200 articles related to blockchain in banking and finance, manufacturing, energy, transportation, and other fields have been submitted. This indicates that researchers continue to be interested in the blockchain topic. Financial institutions and large accounting firms such as Deloitte, PwC, Ernst and Young (EY), and KPMG have seen the potential of blockchain and thus have started several projects in blockchain technology. The researchers identified in this review have explored blockchain in various areas, such as how this technology works, application management and smart contracts, challenges, banking, finance and payment systems, and FinTech (Fig.  9 ). Summary finding of each area has stated in the next section.

figure 9

Research areas (2008–2022), 50 articles, blockchain in finance

4.2.1 Blockchain technology and how it works

Research interest relates to the technology of blockchain refers blockchain as chronologically added immutable chain of cryptographically secured blocks that holds time-spanned transaction on a peer-to-peer network. This data sharing platform accepts the blocks verifying arithmetically produced code called hash that enable tamper proof chain. Researchers pay attention to understand the technology, security, consensus, and mining processors [ 1 , 3 , 4 , 12 , 17 ].

Blockchain technology is evolving, industry is getting ready to embrace the blockchain technology; however, researchers have mentioned that organizations need to leverage research on blockchain technology to better understand the critical insights to optimize the business strategies which assists in decision-making at the strategic level [ 21 , 34 ].

4.2.2 Blockchain application management and smart contract

Bitcoin is the initial application of blockchain [ 24 ]. Research community refers blockchain as potential application for transaction-oriented process and therefore interest in application management in many areas such as supply chain, healthcare, finance, sustainability, and energy [ 17 ]. Furthermore, they suggest exploring the knowledge in application management that relates to the above field. Blockchain application includes transparency, trust, infrastructure, smart contract, and makes sure business availability and continuity. The consensus protocol defines the agreement to add blocks to the blockchain. The most common consensus mechanisms are proof-of-work (PoW), proof-of-stake (PoS), practical byzantine fault tolerance (PBFT), and delegated proof-of-stake (DPoS) [ 17 ].

Blockchain-based finance solutions provide a feasible solution to transaction big data digitization, verification, and monitoring. Smart contracts are computer codes designed to facilitate, verify, and enforce business rules automatically to satisfy the business logic, make sure the reliability, verifiability, and security of financial data on the chain [ 9 , 12 , 17 , 21 , 36 ].

4.2.3 Challenges

Blockchain creates opportunities in many sectors; however, there are risks and challenges related to successful chain implementation. Researchers have identified challenges associated to blockchain in the banking and finance sector in many forms: technical challenges, organizational and user-related, social, and regulatory [ 6 , 7 , 21 , 23 , 26 , 33 , 35 ]. Technical challenges include limited space, less network performance, lack of universal protocols and standardizations, and high-energy consumption [ 2 , 21 , 23 ]. End users challenges include user resisting the technology as its disruptive to the traditional banking and finance process. This alternative workflow requires systematic integrations to gain trust from end users as they worry about privacy, integrity, and security threats [ 6 , 7 , 21 , 23 ]. Social risks create as the technology transforms financial industry and the labor market. Regulatory standardization is one of the significant challenges for government and regulators [ 33 ]. Additionally, the biggest obstacle is the immaturity of the technology [ 2 , 7 , 21 , 23 , 26 ]. Cyber risks, vulnerabilities, hardware requirements related the transaction big data management, mining performance, scalability handling, technical identification, risk identification, implementation difficulties, system integrations, regulatory restrictions, and social acceptancy are the highlights [ 21 , 23 , 26 , 35 ].

At the same time, chained-oriented system in financial field is capable to solve issues related to the finance field such as financial frauds, money laundering, high audit risks, tax avoidance, and cross-border financial variances [ 21 ].

4.2.4 Blockchain in banking

Banking sector facing issues relates to user verification, transaction monitoring, assets management, and cross-border remittance management. Know your customer (KYC) and anti-money laundering (AML) regulations are essential business processors. Banks spent massive amount of money to comply with KYC and AML regulations. Literature suggests blockchain technology as a solution in this requirement. Furthermore, banks require faster, efficient, transparent, secure, intuitive, and cost-effective transaction platform that blockchain is capable to deliver [ 2 , 5 , 8 , 9 , 10 , 11 , 21 ].

4.2.5 Blockchain in finance and payment systems

As research community has identified banking and finance sector has potential to enhance the business processes using blockchain technology [ 9 , 12 , 17 , 21 , 36 ], the banking platforms can be disrupted positively. Financial sector is looking solutions for verifying identification, high cost, slow transaction managing, cross-border transactions, and assets management. Major banks in the world are investigating how to solve the issues using blockchain technology [ 36 ] that has the ability to provide low cost and high-speed real-time monitoring features. Some financial institutes are investigating how to use blockchain to enhance security in the applications [ 21 , 36 ].

4.2.6 FinTech

FinTech is a combination of technology and financial service interest in digitization of financial processes, particularly in banking and finance institutes. Researchers have identified FinTech as a technology enabler of finance industry [ 20 ]. FinTech investments have shown exponential increase with blockchain, AI, and big data technology innovations [ 13 , 14 , 16 ]. Publications considered FinTech in different categories such as organizational structure, products and services, business processors, and as an IT enabler system [ 16 , 20 ], further categorized as blockchain and crowdfunding as subsets of FinTech [ 34 ]. The economic, financial, and business and management areas are the most popular research areas related to FinTech [ 14 , 16 , 20 , 34 ]. In contrast to the findings, researchers have identified FinTech as an IT enabler for business models [ 14 , 16 , 20 , 34 ].

4.3 Blockchain features solve issues in the current financial sector

The maintenance of financial big data incurs significant costs for financial institutions. The current finance sector works on traditional activities that require a central trusted party such as the central bank to verify each transaction. Traditional financial institutions are not interoperable because maintaining ledgers in silos is costly. Furthermore, the lack of transparency, customer access restrictions, and centralized control are some of the problems with current financial operations [ 37 ]. The current centralized payment networks in financial systems, such as SWIFT, PayPal, and Visa, charge a high cost for their services. To ensure compliance and promote sustainability, governing bodies are linked to the current financial systems. Every transaction in the financial industry requires transparency, security, data validity, reliability, and integrity. Financial institutions will be able to give customers a proper service if they can lower the costs.

Blockchain technology addresses issues in the financial sector and offers a single platform that all stakeholders can use to gain a competitive advantage, as shown in Fig.  10 . The enormous benefit of this technology is based on distributed trust that omits the requirement of a third party to manage payments, leading to transaction costs and time reductions [ 36 ]. Moreover, the compliance cost can be reduced, as this gives participants the chance to use a common compliance software package [ 33 ]. However, blockchain transactions cannot be reversed, and all transactions are transparent. This immutability feature aids in maintaining a record of each financial transaction, improving traceability during the audit process, and streamlining the compliance process for financial institutions. Distributed open-source protocols provide integrity that allows transactions to be executed without a trusted third party [ 19 ].

figure 10

Blockchain technology features

Although there is no centrally trusted agent, the consensus mechanisms used in the blockchain are broad and precise, enabling the validity and integrity of transactions that are required in the financial sector. Chang et al. [ 6 ] stated that the “most excellent value of blockchain is a decentralized system, whose security chain is very long.” Therefore, the trusted decentralized ledger enhances the security of business processors by checking the history of transactions without an intermediary agent. Additionally, cryptography can mitigate cybersecurity risks.

Blockchain technology has been implemented in multiple fields and applied to multiple functions. Cocco et al. [ 2 ] and Trivedi et al. [ 12 ] mentioned that blockchain technology can be implemented in the financial sector in different areas such as banking, insurance, risk management, fraud control, e-finances, credit cards, digital payments, and innovations. Blockchain can also be applied to anti-money laundering (AML) and KYC requirements for financial applications [ 6 ]. Therefore, banks and financial institutions have already started to participate in this revolutionized journey in various ways. For instance, the central banks of different countries have begun using blockchain technology in their processors.

4.4 Blockchain still immature due to challenges

This section compiles the issues identified in blockchain implementation in the financial sector. Blockchain is a promising technology, but its potential remains unclear. It followed the same resistance and thoughts raised when the Internet, a technology that has revolutionized communication over computers, was introduced. This is because of the huge number of unknown factors, lack of knowledge, and issues that researchers have not yet resolved. For instance, suspected money laundering activities associated with cryptocurrencies, instabilities, and vulnerabilities of digital currencies are major deterrents [ 33 ]. In the IEEE special issue [ 23 ], it is stated that although technological and business-related blockchain developments and challenges have been identified, engineering and management challenges of blockchain technology have not yet been addressed. Many Bitcoin scandals have decreased the trust, reliability, and accuracy of blockchains. For example, market downfalls, such as Mr. Gox in Japan in 2014, occurred due to a lack of security, and the security breach incident of Bitfinex in Hong Kong in 2016 has dramatically changed the public perception of blockchain technology [ 23 , 33 ]. Cyberspace is full of unknown threats, and protecting business and financial big data is necessary. Consequently, new and growing blockchain security attacks are identified as ledger and consensus-based, smart contract-based, peer-to-peer network-based, and wallet-based attacks. Although blockchain intervenes to address these issues, it is still immature to convince the public to embrace them [ 26 ].

Blockchain technology has not yet attained the highest level of interoperability in the financial sector owing to energy consumption, privacy ethics, user trust, laws and regulations, compliance rules/protocols, supervision, and network integration. Cocco et al. [ 2 ] and other similar studies stated that blockchain consumes more energy and requires high computational power to mine, particularly when the chain is growing rapidly. Cheng’s interviewees claimed that energy consumption depended on the consensus mechanism chosen in the mining process. There are still ethical issues related to privacy on the public blockchain that have not yet been resolved because encoded information in the blockchain is shared with all participants in the network by default. This extreme transparency may jeopardize data privacy. Yeoh [ 33 ] stated that the lack of rules and regulations for compliance and the absence of strategic governance enforcement are the reasons for losing trust in blockchain technology. Chen and Bellavitis [ 3 ] indicated that, although decentralization can work in interoperability among financial institutions, it has not yet received its highest limit of interoperability. Because of the complexity of implementation, high-development cost [ 26 ], knowledge and experience niche, blockchain developer niche, lack of regulators, and lack of communication among stakeholders generate a lack of trust in this technology. Therefore, financial institutions are hesitant to invest in this technology for purposeful solution artifact design [ 38 ] because of the associated risk in system solution for data governance. Chang et al. [ 6 ], Trivedi et al. [ 12 ], and Alam et al. [ 35 ] pointed out that scalability, latency, security, interoperability standards, standardization cost, data protection laws, regulations, legislation, and consensus are not yet at the required level to rely on this technology. As Feldman [ 39 ] summarized in Statista, the biggest barriers to adopting blockchain technology globally are 27% regulatory uncertainty, 25% lack of trust among users, 21% ability to bring networks together, 11% separate blockchain not working together, 6% inability to scale, 6% concerns of intellectual property, and 4% concerns of audit and compliance.

4.5 Future research

The findings show that the blockchain technology is evolving. This paper provides significant insights for both industry and academia to create new research paradigms. The practitioner can explore how the technology can be adopted. We focus on creating a new knowledge about blockchain-oriented solution design which will offer for early researchers with initial understanding how to use blockchain as a component of any smart data solution design. Researchers will achieve a lot of insights about the specific rules and policies in blockchain data solution that are important to be reflected into a solution design. Then, the future research can further analyze how the regulations will affect on the blockchain finance and can be implemented. Another further research may extend block chain and big data research in sub-fields (e.g. higher education [ 40 , 41 ] and electronic government applications [ 42 ]) including healthcare information systems design [ 43 ].

5 Conclusion

Based on the findings of this research, we clarify blockchain technology in finance from three major pillars: business, technological, and social perspectives. There is a research gap in blockchain implementation in the finance sector owing to the lack of knowledge about blockchain technology, research on blockchain technology, and research on blockchain technology in the finance sector. Based on stakeholder needs, blockchain technology can be used in the finance sector to analyze, process, and manage big financial data effectively and efficiently. The main concerns in this implementation process are the financial big data, rules, and applications. Scientific research has identified ways to close these gaps in the application environment of finance blockchains from the perspectives of business requirements, technology, applicability, regulation, and supervision for improving the finance sector. Technical factors, such as network delay during the encryption process and authentication, information transmission, storage problems between modules, and block capacity, are also uncertain gaps. Furthermore, security and privacy breaches that occurred in cryptocurrency platforms demotivated society to believe in blockchain technology. It is widely believed that now is the ideal time for academics, researchers, banks, and other financial institutions to further explore blockchain technology. Additionally, governments should have a strategic plan to deliver blockchain knowledge to industry professionals and create opportunities to explore financial blockchain models.

In conclusion, the future of finance will be dominated by blockchain technology, which society may eventually accept. This can be achieved once the following core areas are addressed. From a business perspective, it must satisfy business, security, and regulatory requirements. From a business technological perspective, the mining hardware requirements must be satisfied. From a business and social perspective, trust must be ingrained in society.

This will push toward transforming the finance sector into a decentralized finance model by reducing transaction costs, increasing transaction scope, avoiding intermediates, increasing transparency of transactions, increasing security, and enabling interoperability and borderless transactions. Although numerous challenges are yet to be addressed, many countries have paid attention to blockchain technology and financial institutions and have been experimenting with blockchain models in finance. The immaturity of blockchain development will be reduced and it will become a new landscape in the financial sector. This is possible if all parties cooperate to meet the commercial, technological, and social requirements related to this technology. As a recommendation, extensive research can be expanded by researching different perspectives in the literature and selecting various parameters, such as blockchain in banking and remittances.

Data Availability

We do not have any additional data to be made available.

The Ripple platform is unknown as a distributed ledger that incorporates a network of validating servers and crypto tokens called XRP, and under the Ripple network, users may develop robust and secure decentralized payment applications to send and receive funds globally over the given blockchain capacity.

Big data simply refers to the huge volume, velocity, and variety of data sets. The financial big data can be defined as huge transactional or other forms of data sets that are large, rapidly accumulating, and complex in nature, therefore require a collection of technologies and methods used to collect, sort, process, and analyze the data sets.

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Weerawarna, R., Miah, S.J. & Shao, X. Emerging advances of blockchain technology in finance: a content analysis. Pers Ubiquit Comput 27 , 1495–1508 (2023). https://doi.org/10.1007/s00779-023-01712-5

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Estimates of the natural interest rate for the euro area: an update

Prepared by Claus Brand, Noëmie Lisack and Falk Mazelis

Published as part of the  ECB Economic Bulletin, Issue 1/2024 .

The natural rate of interest, r* (or “r-star”), is defined as the real rate of interest that is neither expansionary nor contractionary. [ 1 ] In the wake of the 2008 global financial crisis, real interest rates (as measured by deducting inflation expectations from a nominal rate of interest) slumped to exceptionally low levels in advanced economies, including the euro area. They have moved higher only recently as monetary policy was tightened following the post-pandemic surge in inflation.

Available estimates of r* can broadly be classified as either slow-moving equilibrium measures or cyclical inflation-stabilising measures. In its 2021 monetary policy strategy review, the ECB referred to the former as being most relevant for gauging risks of policy rates being constrained by their effective lower bound. Slow-moving measures reflect long-run equilibrium levels, determined by structural factors, towards which real interest rates are gravitating. But over the business cycle slow-moving measures are not a good indicator of the natural short-term real interest rate that eliminates both inflationary and disinflationary pressures. Gauging developments in the natural rate over the business cycle requires a model-based approach which ensures that a central bank tracking r* stabilises inflation either concomitantly (in the textbook New Keynesian Dynamic Stochastic General Equilibrium – DSGE – model, in the absence of nominal frictions) or over the medium term (in econometric models, in the absence of unforeseen shocks).

Inferences about movements in r* are subject to high uncertainty. r* is unobservable and its estimation is fraught with a host of measurement and model-specification challenges. In practice, r* estimates and their interpretation are always model and data-dependent, and thereby subject to both model uncertainty and statistical uncertainty. When assessing movements in r*, the models used and the specifics of the estimation must be taken into consideration. Such aspects include the policy instrument used to measure the monetary policy stance, the measure of economic slack included in the analysis, the level at which inflation stabilises once the slack is absorbed and the time horizon over which this occurs. Whether the lower bound on interest rates or the effects of unconventional monetary policies are factored into the estimation of r* also matters for the statistical validity of the measures.

Cyclical measures of euro area r* differ in their inflation-stabilising properties. Not all cyclical measures offer desirable inflation-stabilising properties. Among the cyclical measures discussed in this box, only a few are obtained from econometric or structural models positing a relationship between a model-specific measure of economic slack, the difference of inflation from the central bank target and the real rate gap (the distance of actual real rates from r*). [ 2 ] In addition, for these econometric model-based measures, the time horizon over which the inflation target is reached can vary greatly with the size and persistence of shocks. Other measures, be they cyclical or slow-moving, model-dependent or survey-based, have even less well-understood inflation-stabilising properties.

The post-pandemic economic environment may have raised cyclical measures of r* but it has also complicated its measurement. The impact of the pandemic, global supply chain disruptions, sharp energy price increases and more interventionist fiscal policies contributed to an exceptional surge in inflation in 2021-2022 and may, in principle, also be associated with a temporary increase in cyclical measures of r*. The inflation surge initially lowered the real short-term interest rate and thereby opened a large negative real rate gap. In principle, this gap has supported increasing economic activity and thereby fuelled inflation further. In addition, to the extent that the post-pandemic recovery in aggregate demand has outpaced productive capacities (which were constrained by further adverse supply shocks), the real rate of interest would have had to increase for this overutilisation of capacities to be corrected, pointing to a cyclically higher r*. [ 3 ] If a higher r* were material and persisted beyond the post-pandemic inflationary episode, it would undercut the effects of the normalisation and tightening of monetary policy since the end of 2021. However, the normalisation of supply in recent years – as seen, for instance, in improvements in delivery times – would work in the opposite direction, reducing the required equilibrium increase in r*.

Slow-moving measures of r* anchored to long-run economic trends are unlikely to have risen recently. While cyclical measures of r* might be edging higher, slow-moving measures of r* that only change over decades are unlikely to have risen, since their long-run economic drivers, such as productivity growth, demographics and risk aversion, have not changed fundamentally. [ 4 ] Productivity growth has remained low and the demographic transition is driving up savings at the global level in anticipation of longer retirement periods. Risk aversion and the scarcity of safe assets have been important factors behind the decline in euro area r* in the wake of the global financial crisis. But it is difficult to gauge how the impact of these factors might be waning over time.

While estimates of euro area r* vary widely across a suite of models, the median estimate has risen by about 30 basis points compared with levels prevailing in mid-2019, before the onset of the pandemic (Chart A). Euro area r* was reported to have fallen to levels around or below zero following the global financial crisis. [ 5 ] Given the uncertainty surrounding r* measures, Chart A reports evidence based on a suite of models and approaches for estimating some slow-moving r* measures and a larger number of cyclical measures of r*, including term structure models, semi-structural models, a DSGE model and survey-based estimates. [ 6 ] Recently the exceptional nature of the pandemic shock has complicated model-based inferences about cyclical measures of r*. Many models have not been amended to factor this shock into the estimation of r*. In a few instances, time averaging of r* or allowing for stochastic volatility in the output gap are used to ensure that the high macroeconomic volatility during this period does not mechanically translate into large r* fluctuations. With this caveat in mind, updated estimates suggest that euro area r* had fallen into negative territory by 2021, with the size of the range of estimates pointing to a very high degree of model uncertainty. Subsequently r* is estimated to have moved closer to pre-pandemic levels – albeit within a narrower range around zero – mainly owing to changes in cyclical measures. Since the second half of 2023 estimates obtained from term structure models and semi-structural models (i.e. excluding the more volatile DSGE model-based estimate) have ranged between about minus three-quarters of a percentage point to around half a percentage point. [ 7 ]

Real natural rates of interest in the euro area

(percentages per annum)

research papers on banking and finance

Sources: Eurosystem estimates, ECB calculations, Federal Reserve Bank of New York and Consensus Economics. Notes: Survey-based estimates include the following: the estimate from the Survey of Monetary Analysts, which is the median of respondents’ long-run expectations regarding the ECB’s deposit facility rate, less expectations of inflation in the long run (starting in the second quarter of 2021); and the Consensus Economics estimate, which is the expected three-month interbank rate ten years ahead, less expectations of inflation in the long run. Term structure-based estimates are derived from Geiger, F. and Schupp, F., “ With a little help from my friends: Survey-based derivation of euro area short rate expectations at the effective lower bound ”, Deutsche Bundesbank Discussion Paper, No 27, 2018; Joslin, S., Singleton, K.J. and Zhu, H., “ A New Perspective on Gaussian Dynamic Term Structure Models”, Review of Financial Studies , Vol. 24, Issue 3, January 2011, pp. 926-970; Ajevskis, V., “ The natural rate of interest: information derived from a shadow rate model ”, Applied Economics , Vol. 52(47), July 2020, pp. 5129-5138; and Brand, C., Goy, G. and Lemke, W., “ Natural rate chimera and bond pricing reality ”, Working Paper Series , No 2612, ECB, Frankfurt am Main, November 2021. Semi-structural estimates are derived from Holston, K., Laubach, T. and Williams, J.C., “ Measuring the Natural Rate of Interest after COVID-19 ”, Federal Reserve Bank of New York Staff Reports, No 1063, June 2023; Brand, C. and Mazelis, F., “ Taylor-rule consistent estimates of the natural rate of interest ”, Working Paper Series , No 2257, ECB, Frankfurt am Main, March 2019 (including stochastic volatility in the output gap, a long-term interest rate, asset purchase effects and the effective lower bound); Carvalho, A., “ The euro area natural interest rate – Estimation and importance for monetary policy ”, Banco de Portugal Economic Studies, Vol. IX, No 3 (based on Holston, Laubach and Williams (2023), with and without inflation expectations); and Grosse-Steffen, C., Lhuissier, S., Marx, M. and Penalver, A., “How to weigh stars? Combining optimally estimates for the natural rate”, Banque de France working paper, forthcoming. DSGE-based estimates are derived from Gerali, A. and Neri, S., “ Natural rates across the Atlantic ”, Journal of Macroeconomics , Vol. 62(C), 2019 (displayed as a three-year moving average of the estimates). The latest observations are for the third quarter of 2023 for Holston, Laubach and Williams (2023), Ajevskis (2020), Grosse-Steffen, Lhuissier, Marx and Penalver (forthcoming), Carvalho (2023), and Geiger and Schupp (2018); and for the fourth quarter of 2023 for all other estimates.

Overall, model uncertainty complicates the measurement of r* and its use as an indicator for monetary policy. While cyclical measures of euro area r* have been edging higher, slow-moving estimates anchored to long-run economic trends are unlikely to have risen. The estimates vary widely, reflecting a high degree of model uncertainty and differences in model-specific inflation stabilisation properties. While these features greatly complicate the use of r* estimates as an indicator for monetary policy at high frequencies, trends in r* estimates still signal risks of nominal interest rates possibly becoming constrained by their effective lower bound in the future.

This box uses the terms “natural” and “neutral” real rate of interest interchangeably. By contrast, Obstfeld (2023) distinguishes between a natural rate – as the real rate of interest prevailing over a long-run equilibrium where price rigidities are absent – and a neutral rate –as the real policy rate of interest that eliminates inflationary and deflationary pressures. However, these two definitions overlap, because neutral measures defined in this way track the frictionless real rate of interest, i.e. they have natural rate characteristics, too. See Obstfeld, M, “ Natural and Neutral Real Interest Rates: Past and Future ”, NBER Working Paper, No 31949, December 2023.

The widely used r* measure from Laubach and Williams (2003) posits a backward-looking relationship between the real interest rate gap, economic slack and inflation. The resulting r* estimate stabilises inflation around a random drift, i.e. not necessarily close to the central bank’s inflation target. See Laubach, T. and Williams, J.C., “ Measuring the Natural Rate of Interest ”, Review of Economics and Statistics , Vol. 85, No 4, November 2003, pp. 1063-70.

Post-pandemic measures of slack are above zero when accounting for the impact of supply shocks on potential output – see the box entitled “Potential output in times of temporary supply shocks”, Economic Bulletin , Issue 8, ECB, 2023.

Cesa-Bianchi, Harrison and Sajedi (2023) draw the same conclusions with respect to global r* developments, for similar reasons. They estimate global r* to have been around or below zero recently. See Cesa-Bianchi, A., Harrison, R. and Sajedi, R, “ Global R* ”, Staff Working Paper No 990, Bank of England, October 2023.

See the Working Group on Econometric Modelling 2018 Report entitled “ The natural rate of interest: estimates, drivers, and challenges to monetary policy ”, Occasional Paper Series , No 217, ECB, Frankfurt am Main, December 2018.

The range of estimates obtained only accounts for model uncertainty and does not take account of much larger statistical uncertainty.

By comparison, for the United States, mixed evidence about developments in r* also highlights model uncertainty. According to the New York Federal Reserve’s DSGE model, between June 2022 and March 2023 the nominal short-term natural rate increased more than the Federal Funds rate (see Baker, K., Casey, L., del Negro, M., Gleich, A. and Nallamotu, R., “ The Post-Pandemic r* ”, Liberty Street Economics , August 2023; and Baker, K., Casey, L., del Negro, M., Gleich, A. and Nallamotu, R., “ The Evolution of Short-Run r* after the Pandemic ”, Liberty Street Economics , August 2023). However, this finding remains debatable as updated estimates of Lubik and Matthes (2015) and estimates from Holston, Laubach and Williams (2023) move in opposite directions. The latter use a semi-structural model assuming that r* is consistent with non-accelerating inflation, while the former do not impose inflation-stabilising properties and define r* as the long-horizon forecast of the real short-term rate in a Vector Autoregression model with time-varying parameters. This resulting discrepancy across recent estimates for the United States also highlights the considerable model uncertainty surrounding r* measures. See Lubik, T.A. and Matthes, C., “ Calculating the Natural Rate of Interest: A Comparison of Two Alternative Approaches ”, Federal Reserve Bank of Richmond Economic Brief, No 15-10, October 2015; and Holston, K., Laubach, T. and Williams, J.C., “ Measuring the Natural Rate of Interest after COVID‑19 ”, Federal Reserve Bank of New York Staff Reports, No 1063, June 2023.

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