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Journal of Financial Crime

ISSN : 1359-0790

Article publication date: 24 January 2022

Issue publication date: 2 January 2023

Banking industry peculiarly has become soft target for several pernicious deceptive and fraudulent activities. The purpose of this paper is to systematically review the literature published in past 20 years on bank frauds and present a holistic view on causes and consequences of bank frauds and measures to curtail this menace. Towards the end the paper provides avenues for future research.

Design/methodology/approach

A systematic literature review approach is used in this study and articles are selected via pre-set inclusion criteria. The literature is mapped on the basis of databases, year of publication, country of study and journal of publication. This paper is based on 70 selected articles published in four prominent databases between 2000 and 2021.

This study reveals that frauds in banking industry have become a matter of grave concern for almost all countries across the globe, causing significant financial and non-financial damages to banks, customers, other stakeholders and economy. Numerous factors such as pressure and opportunity are responsible for fraud occurrence. This study further evinced that banking institutions inevitably should have a robust fraud risk management in place to prevent, detect and respond to defalcation.

Originality/value

To the best of the authors’ knowledge, this is the only paper among 70 selected articles which systematically reviews the literature published in past 20 years and provides a comprehensive view on all aspects related to bank frauds.

  • Banking industry
  • Fraud mitigation
  • Systematic literature review

Acknowledgements

Funding source : This research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors.

Mangala, D. and Soni, L. (2023), "A systematic literature review on frauds in banking sector", Journal of Financial Crime , Vol. 30 No. 1, pp. 285-301. https://doi.org/10.1108/JFC-12-2021-0263

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  • Open access
  • Published: 13 March 2023

Online payment fraud: from anomaly detection to risk management

  • Paolo Vanini   ORCID: orcid.org/0000-0003-0391-4847 1 ,
  • Sebastiano Rossi 2 ,
  • Ermin Zvizdic 3 &
  • Thomas Domenig 4  

Financial Innovation volume  9 , Article number:  66 ( 2023 ) Cite this article

8368 Accesses

6 Citations

Metrics details

Online banking fraud occurs whenever a criminal can seize accounts and transfer funds from an individual’s online bank account. Successfully preventing this requires the detection of as many fraudsters as possible, without producing too many false alarms. This is a challenge for machine learning owing to the extremely imbalanced data and complexity of fraud. In addition, classical machine learning methods must be extended, minimizing expected financial losses. Finally, fraud can only be combated systematically and economically if the risks and costs in payment channels are known. We define three models that overcome these challenges: machine learning-based fraud detection, economic optimization of machine learning results, and a risk model to predict the risk of fraud while considering countermeasures. The models were tested utilizing real data. Our machine learning model alone reduces the expected and unexpected losses in the three aggregated payment channels by 15% compared to a benchmark consisting of static if-then rules. Optimizing the machine-learning model further reduces the expected losses by 52%. These results hold with a low false positive rate of 0.4%. Thus, the risk framework of the three models is viable from a business and risk perspective.

Introduction

Fraud arises in the financial industry via numerous channels, such as credit cards, e-commerce, phone banking, checks, and online banking. Juniper Research ( 2020 ) reports that e-commerce, airline ticketing, money transfer, and banking services will cumulatively lose over $ 200 billion due to online payment fraud between 2020 and 2024. The increased sophistication of fraud attempts and the increasing number of attack vectors have driven these results. We focus on online and mobile payment channels and identity theft fraud (i.e., stealing an individual’s personal information to conduct fraud )(Amiri and Hekmat 2021 ). The aim is to identify external fraudsters who intend to initiate payments in their interests. As fraudsters gain access to the payment systems as if they were the owners of the accounts, they cannot be identified based on the account access process. However, the fraudster behaves differently during a payment transaction than the account owner and/or the payment has unusual characteristics, such as an unusually high payment amount or transfer to an account in a jurisdiction that does not fit the life context and payment behavior of the customer. The assumption is that algorithms can detect anomalies in behavior during payment transactions.

West and Bhattacharya ( 2016 ), Abdallah et al. ( 2016 ), Hilal et al. ( 2021 ), and Ali et al. ( 2022 ) reviewed financial fraud. They found a low number of articles regarding online payment fraud. For example, Ali et al. ( 2022 ) cited 20 articles on financial statement fraud and 32 articles on credit card fraud, see Li et al. ( 2021 ) for credit card fraud detection. Online payment fraud was not listed. The reviews also clarified that many articles utilized aggregated characteristics. However, we emphasize that fraud in online payments can only be detected based on individual data, as such fraud can only be detected through the possible different behavior of the fraudster and the account holder during payments. As fraudsters learn how best to behave undetected over time, they adapt their behavior. Therefore, self-learning defense methods are expected to outperform static-rule-based algorithms. The correctness of the expectation was shown by Abdallah et al. ( 2016 ) and Hilal et al. ( 2021 ). Various machine-learning algorithms for fraud detection have been proposed in the literature, including decision trees, support vector machines, and logistic regression to neural networks. Aggarwal and Sathe ( 2017 ) discussed various methods for outlier ensembles, and Chandola et al. ( 2009 ) provided a taxonomy and overview of anomaly detection methods.

A common feature in many studies is imbalanced data (i.e., the low proportion of fraud events in the dataset, see Wei et al. 2013 ; Carminati et al. 2015 ; Zhang et al. 2022a ; Singh et al. 2022 ). Risk detection involves detecting fraudulent transactions and stopping them before execution.

In addition to the efficiency of the algorithms, the data basis is an important reason for the differences in fraud-detection performance. While many studies have utilized either often less rich synthetic or Kaggle data, we were able to work with real data. Log files, which have substantial information content in our work, are hardly expected in Kaggle data. The difference in the data complexity is also reflected in the number of features. Singh et al. ( 2022 ) showed that the feature space consists of 31 features compared to our 147 features. Moreover, the proportion of fraudulent transactions in Singh et al. ( 2022 ) is more than a hundred times higher than in our case. Consequently, our data are much more unbalanced than any other study we know of, and the task of finding efficient fraud detection algorithms is more difficult.

However, limiting risk management to the optimal detection of anomalies does not ensure that losses caused by fraud are minimal. Optimal fraud detection can be economically suboptimal if, for example, it is efficient for small amounts of money but unsuccessful for large amounts. Thus, the machine learning outputs for risk identification must be optimized from an economic perspective. We call this optimization the triage model. Yet, neither fraud detection nor the triage model can provide an answer to the question of how large the losses in a payment channel are. Therefore, we develop a statistical risk model that considers the effects of countermeasures on loss potential. The risk model provides risk transparency and makes it possible to assess which measures in the fight against fraud in various payment channels make sense from an economic and risk perspective.

Literature on fraud risk models often refers to qualitative or assessment models for assessing fraud risk or risk assessment models (Sabu et al. 2021 ). We are not aware of any quantitative fraud risk management framework that explicitly considers the impact of the fraud detection process statistically in risk modelling. For organizational, procedural, and legal risk aspects, we refer to the literature. Montague’s ( 2010 ) book focuses on fraud prevention in online payments but does not consider machine learning and risk management in detail. The Financial Conduct Authority’s Handbook (FCA 2021 ) provides a full listing of the FCA’s legal instruments, particularly those relating to financial crime in financial institutions. Power ( 2013 ) highlights the difference between fraud and fraud risk from historical and business perspectives. Van Liebergen ( 2017 ) looks at “regtech” applications of machine learning in online banking. Fraud risk events in cryptocurrency payment systems are different from the online banking cases under consideration; see Jung et al. ( 2019 ) for fraud acting on a decentralized infrastructure and the review article of Trozze et al. ( 2022 ).

The development and validation of the three linked models are the main contributions of our work. To our knowledge, this is the first study to develop, validate, and link components of the risk management process. The output of the anomaly detection model (i.e., the ROC curves), is the input for the triage model, which provides economically optimized ROC curves. Fraud statistics data were utilized to calibrate the various components in the risk model. With these three models, the fraud risk management process can be qualitatively implemented at the same level as the risk management of market or counterparty risks (see Bessis ( 2011 ) to describe risk management in banks).

The performance of our risk management framework is the second contribution, although the performance comparison of our fraud detection method with the literature is limited and cautious, due the use of synthetic data instead of real data, a consideration of different channels in payments with different behavioral characteristics of bank customers, and the publication of incomplete statistics. Nevertheless, we compared our work with Wei et al. ( 2013 ) and Carminati et al. ( 2015 ), both of which analyze online banking fraud based, in part, on real data. The true positive rate (TPR) at a false positive rate (FPR) of \(1\%\) was \(45\%\) . In Wei et al. the TPR is between \(49\%\) and \(60\%\) , but unfortunately, the FPR is unknown. In the relevant scenario of Carminati et al. ( 2015 ), the TPR is \(70\%\) with an FPR of \(14\%\) . This FPR is not acceptable to any bank. Processing by specialists leads to high costs. We discuss all these statements in detail in the " Validation results " section. Considering all three models, the theoretical and practical importance of our approach becomes clear. The expected losses in a scenario of CHF 2.023 million, which utilizes the results of machine learning without economic optimization in the triage model, common in the literature, are reduced to CHF 0.800 million with the triage model (i.e., a reduction in the loss potential by more than 60% follows). In addition, if fraud detection is implemented without a risk model, fraud risk can be massively overestimated. Applying our models to three different payment channels, the overestimation of risk ranged from 54% to over 700%.

The remainder of this paper is organized as follows. In " Fraud risk management framework " section, the model selection for the fraud risk management framework is motivated and described. In " Online payment fraud anomaly detection " section, we consider the anomaly-detection model. " Fraud detection triage model " section links fraud detection to an economic perspective utilizing the triage model. " Risk model " presents the statistical risk model. " Conclusion " section concludes.

Fraud risk management framework

We provide an overview of the three interrelated quantitative models in the context of risk management: online payment anomaly detection, triage model, and risk model.

Online payment fraud anomaly detection

The goal of anomaly detection is to detect fraudulent activities in e-banking systems and to maintain the number of false alarms at an acceptable level. The implementation of the model consists of three steps: pre-filter, feature extraction, and machine learning.

Non-learning pre-filters ensure that both obvious fraud and normal transactions are sorted early to reduce the false positive rate. Only transactions that pass the pre-filter step are passed on to the machine-learning model. Banks utilize non-customer-based static if-then rules, such as blacklists or whitelists. Pre-filters free the algorithms from obvious cases. The adaptability and flexibility of the machine-learning model is necessary to counter the ever-improving attacks of fraudsters with effective fraud detection.

Our data face the following general challenges in payment fraud detection (per Wei et al. 2013 ): large transaction volume with the need for real-time fraud detection, a highly imbalanced dataset, dynamic fraud behavior, limited forensic information, and varying customer behavior.

Given the extremely imbalanced data, fully supervised algorithms typically struggle. Aggarwal and Sathe ( 2017 ) proposed unsupervised and weakly supervised approaches based on features that encode deviations from normal behavior. For each customer participating in an e-banking session, we assess whether the agent’s behavior is consistent with the account holder’s normal behavior. The key information for behavioral analysis lies in the sequence of the customer’s clicks during the session. We show that, unlike online e-commerce transactions (see Wei et al. 2013 ), transaction data, customer behavior data, account data, and booking data are also important for the performance of the algorithm. More precisely, these features are divided into behavioral, transactional, and customer-related features. Starting with nearly 800 features, 147 were extracted utilizing a Bagged Decision Tree Model (BDT). These numbers are many times higher than those for credit card fraud with one to two dozen features (see Table 8 in Hilal et al. 2022 ). The high dimensionality of the feature space also arises in machine learning corporate default prediction models, where several steps are needed to extract all noisy features (see Kou et al. 2021 ).

Our e-fraud model operates according to the following principles.

The model learns the “normal” behavior of each customer based on historical payment data and online banking log data.

Each new transaction is checked against the learned “normal” behavior to determine if it is an anomaly by extracting the 147 features from the data.

If an anomaly is detected, it is flagged as suspected fraud.

Detected transactions that are not found to be fraudulent after manual review are reported back to the model for learning purposes.

As there are very few known fraud cases, all base learners are trained on fraud-free data only in step one. Fraud cases are only utilized in step two of ensemble aggregation when the base learners are combined to form the final predictive function. The first step is to define base learners who are rich enough to detect a wide range of suspicious transactions or online user sessions. We consider three base learners: the density-based outlier detection model (unsupervised, Local Outlier Factor (LOF)), the isolation-based outlier detection model (unsupervised, Isolation Forest (IF)), and a model for normal customer behavior (supervised, Bagged Decision Trees (BDT)) as base learners (see Breunig et al. ( 2000 ), Chandola et al. ( 2009 ); Zhang et al. ( 2022b ) for LOF, Liu et al. ( 2012 ); Tokovarov and Karczmarek ( 2022 ) for IF). We refer to individual instances of LOF, IF, or BDT as base learners. The BDT model is not only a base model, but it is also utilized for feature selection in the other two base models: LOF and IF. The LOF method is suitable for outlier detection, where each observation is assigned an outlier level based on its distance from the nearest cluster of neighboring observations. The aim is to detect outliers in inhomogeneous data, for which classical global outlier methods typically do not provide satisfactory results. Conversely, IF explicitly isolates anomalies without capturing all normal instances. These two methods consider the heterogeneity in the data.

In the second stage, the base learner’s fraud score was aggregated. We consider two approaches to determine the weights in the ensembles: a simple averaging and a supervised approach, although our model largely consists of unsupervised procedures because of the limited availability of fraud cases for which we can extract all the required features. However, we introduce supervision where we utilize scarce labelled data to adjust the importance of certain base learners in the voting scheme, ultimately deciding whether an observation is fraudulent. The penalized logistic regression chosen for classification allows for a better interpretation of the model, as the weights can be utilized to identify base learners, subsets of features, and subsets of samples that have been particularly useful in detecting a particular type of fraud.

Triage model

The fraud detection model calculates scores and, in comparison with a threshold value, decides whether a transaction is flagged as an anomaly. This process results in the probability of detection for a given investigation effort, as indicated by the ROC curve. By making the threshold dependent on the transaction size, we can ensure that larger transaction amounts are more likely detected than smaller ones. This gives up part of the true positive rate (TPR) to reduce overall economic losses (i.e., the TPR decreases for a given FPR). This economic optimization that leads to adjusted ROC curves defines the triage model.

To minimize expected cumulative losses, the constant fraud anomaly detection threshold becomes a function of the transaction amount. Here, the transaction amounts are random variables whose distributions are estimated. In the optimization problem, the transaction function is chosen to maximize the average cumulative sum of the detected fraudulent transactions, where the expected FPR must not exceed a certain threshold. Utilizing this optimal threshold function, the fitted ROC curves were obtained.

The optimization problem has a unique solution if the ROC curve is a concave function of the false positive rate function of the threshold and if the acceptance set of the expected false positive function constraint is convex. With the chosen piecewise linear false positive constraint function, the assumptions regarding the existence of an optimum are satisfied. The ROC curves that result when fraud anomalies are detected serve as inputs for the optimization. However, because only a vanishingly small number of fraud cases exist, the TPR values for certain FPR levels are subject to considerable uncertainty. Hence, cubic spline functions were utilized for the ROC curve of the optimization.

The UK Finance (2019) report states that the recovery value for online and mobile banking in the UK is 18% of the potential loss. Therefore, we introduced an extension to the optimization program to include recovery.

Losses from transaction fraud are included in operational risk incurred by banks. As for other operational risks, one of the key questions from a risk-management perspective is whether the allocated resources and countermeasures are adequate. To answer this, one needs some way of quantifying the risk incurred, ideally a Value-at-Risk (VaR)-model that fits the general risk framework of the bank. Simply, the model calculates the loss \(L=E(\lambda )\times E(\tau )\) where \(\lambda\) is the expected event frequency (fraud), and \(\tau\) is the expected loss per event. The challenge is to determine the distributions of these variables in a tractable and plausible manner and define a model while having very scarce data on past events. We chose the path of an aggregated simulation of many scenarios per e-channel to account for the inherent uncertainty in the choice of these parameters.

Unlike market or credit risk, fraud risk is borne by comparatively few individuals or groups who utilize very specific strategies and technologies to overcome vulnerability in the payment process. Simultaneously, defenders analyze attack plans and update their countermeasures. In this constantly changing environment, neither the frequency of attacks nor the transaction amounts can be assumed to be statistically regular with great certainty. Therefore, we propose a simple, flexible stochastic model composed of basic building blocks. With such a model, risk managers can quickly adjust the model as needed, perform what-if analyses, or simulate changes in payment infrastructure.

The basic model structure for the e-fraud risk model consists of (i) independent models for the three channels, whose components and parameters can be flexibly assembled and adjusted, (ii) sub-models in each channel based on three model types, and (iii) a recovery model for each channel. The three model types for the three different online payment channels in this study are a Beta model (restricted distribution of transaction amounts), a Generalized Pareto Distribution (GPD, unrestricted distribution of transaction amounts), and a “mass attack model” (many simultaneous Beta-type attacks).

Countermeasures against fraud and recovery measures after fraud events play an essential role in determining risk potential. Therefore, they were integrated into the risk models. Countermeasures against online fraud can be divided into those that strengthen the general infrastructure of the payment process and those that focus on defense against actual attacks. The former is conceptually part of the risk model described above, as it affects the frequency and possibly the transaction size of the attacks. However, the latter is better understood in the context of recovery and is considered in the triage model.

Raw data consisted of transaction data, interaction data between customer and e-banking interface, account, booking, and customer reference data. All users with fewer than 10 logged online sessions were removed as input for ensemble learning. The removed cases were handled separately by utilizing a case-back model.

The transaction history in our dataset consists of 140 million transactions over three years. One hundred fraud cases are reported, but only 11 cases can be linked to the recorded 900’000 online session logs: a \(0.0012\%\) fraud rate. Only 900’000 of the 140 million transactions were possible as the log files were only stored in the bank for three months. This change occurred after the project.

A feature vector is created for each e-banking session based on raw data. The interaction pattern features consist of n -grams constructed from customers’ request sequences and normalized deviations from the expected duration between each pair of consecutive requests sent by a customer in an online session. Particular attention was paid to the typical time required to complete a two-step verification process during enrolment. Payment pattern characteristics were calculated for weekday, weekly, and monthly seasonality. These include normalized deviations from the expected payment amount and remaining account balance. Technical data included the IP address of the online session, the HTML agent, and the number of JavaScript scripts executed. Finally, we utilized historically observed confirmed fraudulent transaction identifiers as the ground truth for the weakly monitored part of the pipeline.

Several quality checks were performed. Consistency tests ensure that the session interaction data and transactions match, for example, that the account exists or that a recipient is listed in the transaction. We also checked for missing or non-parsable values, the latter removed.

The data are extracted from several different data sources within the bank in a two-step Python extract, transform, and load (ETL) process, and converted into features for the algorithm. First, we introduce all raw data into a standard structured format for all data sources. Then, we perform the feature engineering described in the following sections to compute the data input for the ensemble.

Our fraud rate \(0.0012\%\) is much lower than that reported in the literature. The figures in the two online banking fraud papers, Wei et al. ( 2013 ) and Carminati et al. ( 2015 ), are \(0.018\%\) and \(1\%\) , respectively. For credit card fraud, the fraud case number is larger, such as \(2\%\) in Piotr et al. ( 2008 ). Outside the banking fraud sector, anomalies account for up to 40% of the observations (see Pang et al. 2020 ). Similar numbers hold in Zhang et al. ( 2022b ), who tested their ensemble-based outlier detection methods on 35 real datasets from various sectors outside the financial sector, with an average fraud rate of 26%.

Feature extraction

For weak supervision, we utilized historically observed confirmed fraudulent transaction identifiers as the ground truth. For training and inference, we created a feature vector for each e-banking session based on the raw data. Each feature aims to encode deviation from expected (“normal”) customer behavior, as observed in historical interactions with the online banking interface and executed transactions. Three types of features are considered.

Behavioral features

The underlying motivation for utilizing features derived from customers’ online session logs is that a large fraction of online payment fraud involves hijacking, where a foreign agent (human or robot fraudster) takes control of the e-banking session. Consequently, it is expected that the timing and sequence of requests posted by the user in a fraudulent session will be significantly different from those in a non-fraudulent session. We utilize the information about user request types (e.g., “get account balance”, “create payment”) and corresponding timestamps to construct the following features:

Normalized n-gram frequency of user’s requests within the online session. We utilized single, pairs and triplets of consecutive requests (1-, 2- and 3-grams) to derive a fixed-size representation. We performed normalization by dividing the number of occurrences for each request with the total number of requests in each session.

Normalized time between consecutive user request n-grams. For each pair of recorded consecutive n-grams, we transformed absolute time between them into deviations by computing z-scores relative to respective historical observations.

Technical attributes of a session (e.g., IP address of the online session, HTML agent, screen size, and the number of executed Javascript scripts) in binary format - 0 if previously observed, otherwise 1.

Transactional features

Transactional features aim to quantify how anomalous aggregate payments scheduled in an online session are compared with previously executed payments and the remaining balance on the account. They are designed to capture attempts to empty a victim’s account through a single large or many small transactions, while being mindful of seasonal patterns (e.g., holidays, travel expenses, bills, etc.).

Normalized ratio of the payment amount relative to remaining account balance. We normalize by computing z-scores relative to historically observed ratios.

Deviation of the scheduled payment amount from the seasonally expected amount. We compute four deviations per session using z-scores relative to historical payments executed in the same hour of the day, day of the week, week of the month, and month of the year, respectively.

Scheduled time for payment execution in binary format - 0 if immediate, 1 if lagged.

A short payment history and many accounts with relatively infrequent transactions proved detrimental to seasonality modelling, hence, these features were omitted from the final model.

Customer-related features

Customer-related features provide insight into peer groups, relationships with other customers, and the service packages they utilize. These include:

Sociodemographic (e.g., age, nationality, profession, and income)

Seniority of client relationship

Product usage (i.e., savings, investment accounts, and mortgages)

Relationship to other customers (i.e., shared accounts, spouses, and family)

These features were not considered within the scope of our study because of data limitations and time constraints.

Functionality and structure of the fraud model

Base learner: bagged decision tree.

Bagged decision trees (BDT) are trained utilizing the concept of transfer learning, which assumes that distinguishing between the behaviors of different clients within their online sessions is a related problem in distinguishing between fraudulent and non-fraudulent sessions. The underlying motivation considers that a large fraction of online payment fraud involves hijacking or when a foreign agent (human or robot fraudster) takes control of the e-banking session. As fraudulent sessions are rare and non-fraudulent sessions abound, the utilization of transfer learning enables the extraction of custom patterns from a much broader dataset and the use of supervised learning. Transfer learning comprises two phases. A learning phase where one discriminates behavioral characteristics of each customer vs. non-customers and a prediction phase where discrimination between non-fraudulent and fraudulent users sessions were considered in this study. The “non-customer” class label is then attributed to fraudulent behavior.

The decision function in BDT base learners is the probability that an observation, a planned transaction x , is a “non-customer behavior.” This value is equal to the average probability observed for all decision trees in the forest.

where M is the number of trees in the bagged forest, \(c_j\) is the corresponding customer behavior class, and \(P_i( Y\ne c_j|X=x)\) is the probability that observation x is a “non-customer behavior” as predicted by the i th tree. Customer behavior class \(c_j\) consists of the collected sessions and transactions, excluding potential fraud. The model was fitted as follows:

For each customer \(c_j\) , we collect the associated sessions, excluding the potential fraud cases. This set represents the “behavior of customer \(c_j\) .”

From the pool of all customer sessions \(C_{-j}\) (excluding \(c_j\) ) we draw a uniform sample of observations to generate a set representing the class “behavior of customer \(c_j\) not,” \(\not c_j\) for short, and equal in size to the \(c_j\) set. Equal sampling is performed to ensure that none of the other customers are overrepresented in \(\not c_j\) .

Each bagged forest in the ensemble is trained on a matching feature subspace utilizing all these observations. The forests consist of 100 decision trees each.

BDTs provide variable selection as an additional benefit, owing to the large amount of data involved in supervised classification. Therefore, they can be utilized to estimate the importance of variables and adequacy of feature engineering. We achieve this by calculating the Gini impurity decreases at each split point within each tree for each feature. Gini impurity is a measure of the likelihood that a randomly selected observation would be incorrectly classified by a specific node m :

where \(p_{mi}\) is the portion of samples classified as i at node m . These impurity decreases were averaged across all decision trees and outputs to estimate the importance of each input variable. The greater the decrease in impurity, the greater the importance. Utilizing these results, we can identify the relevant subsets of input variables.

Relying on the concept of transfer learning (if the problem described in ?? is sufficiently like fraud detection), we use BDT to select a subset of \(N=147\) features. Particularly important were features measuring the deviation from the typical time required to complete a two-step verification process during login. Features that encode the relative time between n -grams and specific user request sequences are important. The following additional base model was built using the features selected by BDT.

Base learner: local outlier factor

The Local Outlier Factor (LOF) detection method assigns an outlier level to each observation based on its distance from the nearest cluster of neighboring observations (Breunig et al. 2000 ). The general intent of the LOF model is to identify outliers in the interior region of data, for which classical global outlier methods and the other considered algorithm, isolation forest, usually do not provide satisfactory results. The LOF decision function is as follows:

with K a k -neighborhood and LD ( x ) the local reachability distance density from x to its k -th nearest neighbor. We fit the model for each customer by collecting the first associated sessions or transactions, excluding potential fraud. Each LOF in the ensemble is created utilizing all these observations on a subspace of the relevant features selected by BDT and the sampled hyper-parameter. Finally, each time a new observation is available, its decision function value is computed regarding the observations from the training set.

Base learner: isolation forest

The IF algorithm recursively splits the data into two parts based on a random threshold, until each data point is isolated. The algorithm randomly selects a feature at each step, and then randomly selects a division value between its minimum and maximum values. The algorithm filters out data points that require fewer steps to be isolated from the entire dataset. In our case, IF separates one observation from the rest of a randomly selected subsample of the original dataset (Fei Tony et al. 2008 ). Anomalies are instances with short average isolation path lengths. The IF decision function is

where E ( H ( x )) is the average number of edges traversed to isolate the node and C is the average number of edges traversed in an unsuccessful search. To fit the model, we first collected each client’s associated sessions or transactions, excluding potential fraud cases. In step two, each isolation forest in the ensemble was created utilizing all these observations in a matching feature subspace. Each forest consisted of 100 isolated trees. Finally, each time a new observation was available, its decision function value was computed regarding the isolation trees created based on the training set.

Base learner scores combination

The decision functions of the base learners produced by our ensembles must be combined into a single fraud score. As these are in different ranges and scales to render the decision functions comparable, we first replace the original scores by their ranks, regarding the non-fraudulent training scores. Rank normalization is more robust and numerical stable as opposed to z-scores, for example. Therefore, we replaced the original scores with their ranks regarding the non-fraudulent training scores for each base learner:

where V is the set of all \(\delta _{\text {Base}}(p )\) over all observations \(p'\) in the learners’ training subsample, with Base being LOF, IF, or BDT.

Owing to the few fraud cases, our model largely consists of unsupervised procedures. However, we introduced supervision utilizing scarce labelled data to readjust the importance of particular base learners in the voting scheme, ultimately deciding whether an observation is fraudulent.

The following score combination procedure was established. First, a training set comprises all fraud cases in the sample, along with healthy transactions uniformly sampled over customers from the ensemble training data. Second, a logistic regression is trained to classify observations as fraudulent or not utilizing the 6 N normalized decision function features and the known fraud status on past transactions as the label.

The following binary-class penalized cost function is minimized:

where \(y_i\) is the fraud label of transaction i , \(X_i'\) is a row of 6 N decision functions describing transaction i , R is the regularization factor, and ( w ,  c ) is a set of weights defining the decision boundary between the two classes: fraud and non-fraud. To account for the imbalance between fraud and non-fraud transactions in our sample, we assign asymmetric penalties for fraud misclassification, as opposed to non-fraud classification.

Choosing logistic regression to optimize the weights of the base learners ensures that the final score combination \(x'w\) represents the log-odds of an observation to be fraudulent.

where \(\delta (x)\) is the probability that the observation x is fraudulent. Finally, to assign a fraud label to a session x , we compare the output combined score, or equivalently, the probability \(\delta (x)\) , to a threshold y , which is chosen based on ROC curve analysis such that the defined maximum allowed false positive rate is not exceeded. The decision boundary of logistic regression is linear, where each base learner is assigned a weight \(w_i\) to determine its relative importance for fraud classification. This structure simplifies the interpretation of the model because these weights can be utilized to identify the base learners, feature subsets, and sample subsets, which are particularly useful in detecting a particular type of fraud associated with a high weight \(w_i\) . Appendix A provides a detailed description of the ensemble design.

Normal customer behavior model

In summary, we created an ensemble model for each client, which is re-trained with new data at regular intervals and can be described by the following steps:

We consider the disjoint sets of behavioral features on session observations and transactional features on transaction observations.

For each of the two features/observations-pairs, we define \(N=1000\) learners for each of the three model categories as follows.

We fix N random sub-samples of features from the feature set. Each sub-sample remains fixed for all customers.

For each customer, we fix N random observation samples from the customer-specific sessions or transactions observations.

For each of the three model categories, for each customer, and for \(i=1,...,N\) , a base learner is defined by applying the model algorithm to the i -th features sub-sample and i -th observations sub-sample. Thus, this results in 6 N base learners per customer, 3 N for sessions, and another 3 N for transaction data.

The decisions for the three base learners are aggregated utilizing supervision, where the knowledge obtained from existing fraud cases is utilized to adjust the base learner weights.

Utilizing this representation, we train a model that (i) outputs an indicator of how likely a scheduled transaction is fraudulent, (ii) aggregates the overall provided decision functions to derive the unified hypothesis, while assigning more importance to learners that showcase the capability to better distinguish between fraud and non-fraud, and (iii) deals with a large imbalance in class representation.

Validation results

The training, test, and validation sets consisted of data collected from July to October 2017, as dictated by the availability of online session log files. Around 900’000 sessions formed the dataset.

Raw data were processed using ETL to derive customer-specific feature representations of each recorded online session. The data were then split into non-fraud and fraud sets. The fraud set was not utilized to train the unsupervised base learners. Non-fraudulent (“healthy”) sessions were separated into training and test sets utilizing a 3-fold cross-validation split. We then sequentially trained the models on each derived training fold and computed the scores for observations in the corresponding test folds. Following, we obtained an out-of-sample decision function value for each healthy session and each base learner. We then assigned base learner scores to each fraudulent session utilizing base learners trained on all healthy data.

The out-of-sample logistic regression decision function values were aggregated by averaging within their respective ensembles (LOFs, IFs, and BDTs). This step yields a 3-dimensional representation of each customer’s online session. Finally, we utilized leave-one-out cross-validation to report the ROC curve measures. Hence, the logistic regression model is consecutively trained on an all-but-one observation, followed by computing the probability of an observation that was left out. Thus, we again obtain an out-of-sample fraud probability for each observation in the sample. We opted for leave-one-out cross-validation to maximize the number of fraudulent observations in each training set, because these are particularly scarce. Once we have obtained the aforementioned out-of-sample probabilities for each observation, we construct an ROC curve to display the FPR and TPR relationship depending on the decision threshold.

Resultantly, when utilizing no transaction data, the detection rate of the machine learning model was in a realistic range of 18% true positives. These primary results can be easily optimized to increase the TPR and simultaneously reduce the FPR utilizing different measures. This led to an increase in true positives by up to 45%, see Table  1 .

Overall, LOF seems to perform best over the entire dataset compared to IF and BDT. However, BDT has a slightly steeper ROC curve at the beginning, thus showing better pure outlier detection capabilities. Furthermore, because BDT seems to detect frauds, as discussed below, involving larger amounts than those detected by LOF, we cannot conclude that LOF outperforms the other approaches considered. Aggregating the decision functions of the ensembles utilizing simple means outperformed supervised aggregation. Through analysis of logistic regression weights assigned to each ensemble of learners, we determined that significantly higher weights were assigned to the LOF ensemble, most likely due to its best performance over the whole dataset. This dampened the input from the other two ensembles. However, this is not the case when the mean for aggregation is utilized. The results were affected by the small number of frauds and the size of the sample analyzed.

Different ensembles detected different types of fraud, and by observing figures depicting money saved per raised alarm, we see that different ensembles (LOF and BDT) detect different types of fraud cases, displayed by a large difference in saved money per trigger. Logistic regression supervision alarms were affected mainly by LOF, thus making it miss large embezzlement detected by the BDT ensemble. This motivates the triage model described in the next section. As of the restriction of the FPR to no greater than 2%, the entire ROC curve is of less interest. The ROC AUC values for the LOF ensemble is 0.93, for the BDT ensemble 0.82, and for the mean decision function ensemble 0.91.

We compared our results with those of Wei et al. ( 2013 ), and Carminati et al. ( 2015 ). These are two of the few studies dealing with online fraud detection that use real-world data, at least in part. Wei et al. ( 2013 ) utilized an unsupervised approach, whereas Carminati et al. ( 2015 ) utilized a semi-supervised approach. Table  2 compares the performance of our model with those of Wei et al. ( 2013 ) and Carminati et al. ( 2015 ). The results of this table should be interpreted with caution. First, different payment channels were considered. Second, the data of Carminati et al. ( 2015 ) were anonymized and did not include fraud cases. These are artificially added to tune \(1\%\) of the data volume, compared to \(0.018\%\) in Wei et al. ( 2013 ) and \(0.0012\%\) in our dataset. Third, Wei et al. ( 2013 ) did not report the FPR. Finally, Carminati et al. ( 2015 ) published an almost perfect error detection for scenarios I + II, but in scenario III, the false positives are too high; they generate too much manual work for the bank. The former scenarios are simple fraud scenarios that would be blacklisted and filtered out in our data before machine learning engages.

Fraud detection triage model

Formalization.

We formalize the triage model and denote by \(\Omega\) the set of all transactions with \(\omega\) as a single transaction, \(T(\omega )\) as the transaction amount function, and \(\chi _F(\omega )\) as the fraud indicator function, where \(\chi _F(\omega )=1\) represents fraud. Space \(\Omega\) is attributed to a probability distribution P with p ( x ) as the density function of the transaction amounts. The threshold value L of the fraud score function S is a function of the transaction amount x . We define:

If we assume stochastic independence of the transaction amount T , score S and fraud indicator \(\chi _F\) , we obtain the following interpretation:

Note that the assumptions of independence are strong, as transaction sizes are utilized as the input of the machine-learning model underlying the score. Conversely, the independence of \(\chi _f\) and T implies that the transaction amounts of fraudulent transactions have the same distribution as those of non-fraudulent transactions. In the context of the value-at-risk model in the next section, we argue that there is little evidence to support this. This considered, the assumption of independence is theoretically difficult to uphold, but in practice quite necessary to obtain our results.

We formulate our optimization problem as follows:

under the constraint of the integrated FPR

The expectation in ( 11 ) is the average cumulated sum of fraudulent transaction amounts detected by the detection model. By letting \(q_0:=E(\chi _F)\) and utilizing ( 10 ), we can rewrite it as

The constant \(q_0\) is irrelevant to the optimization. Setting \(g(x)=\text {FPR}(L(x))\) , we reformulate the optimization problem in terms of the ROC curve as

under the constraint:

To account for the recovery, we introduce a recovery function \(\theta :\Omega \rightarrow [0,1]\) . This function changes the objective function in the optimization problem, as follows:

whereas this constraint does not change.

Optimization

To put our formal model into practice, we need to fix a distribution for transaction amounts. Utilizing approximately 12 million transactions from online banking and 1.2 million from mobile banking, we approximated the distribution for both channels utilizing lognormal distributions. Although this choice does not particularly focus on the distribution’s tails, it will be seen that the optimal model still places strong emphasis on the detection of anomalies with large transaction amounts. Some basic statistics of the fitted distributions are given in Table  3 .

The ROC curve is conceptually the output of the detection model described in the previous section. However, owing to the limited number of actual fraud cases available, the TPR values for the given FPR levels are tainted with considerable uncertainty. The ROC curve utilized in our optimization was obtained by fitting a cubic spline function to the base points, as presented in Table  4 . The support points were adjusted to avoid unwanted spikes in the cubic interpolation.

As the triage model aims to prevent large losses with a higher probability than smaller ones, the optimal FPR will be an increasing function of the transaction size. To avoid possible optimization problems, we choose a simple form for FPR as a function of transaction size, namely, a piecewise linear function satisfying \(g(0) = 0\) , \(g(T_1) = a\) , \(g(T_2) = 1\) for the parameters \(a > 0\) and \(0< T_1 < T_2\) (see Fig.  1 ).

The optimization problem can be simplified by assuming equality in ( 12 ) and solving a as a function of \(T_1\) and \(T_2\) . For a target integrated FPR of 0.4%, we obtained the solutions listed in Table  5 .

Figure  1 illustrates the results for an online banking channel. The concave shape of the FPR curve up to \(T_2\) shows that the optimal solution emphasizes the detection of large transaction fraud cases, accepting, in turn, the less rigorous testing of small and moderate transactions up to \(T_1\) . For transaction amounts larger than \(T_2\) , FPR and TPR are equal to 1 by construction. Hence, all such transactions are automatically flagged as anomalies.

Total Effectiveness

is the average percentage of integrated fraudulent transaction amounts detected as anomalies. In our optimized case, the rate was 39%.

figure 1

Panel Left: False positive rate as a function of the transaction amount under the constraint that the total false positive rate is smaller than 0.4 percent. Right Panel: True positive rate as a function of the transaction amount. The total effectiveness is 39 percent

Compound Poisson processes were utilized as basic building blocks. We utilize beta marginal distributions for modelling bounded transaction amounts and generalized Pareto marginal distributions (GPD) for unbounded ones. The so-called mass-attack model is formulated as a nested compound Poisson process with a marginal beta distribution. All subprocesses are aggregated independently. Loss statistics, such as value-at-risk or other quantiles of the distribution, are obtained by running Monte Carlo simulations.

Utilizing the limited available fraud data and drawing on discussions with practitioners, we develop the following model for online banking fraud:

Isolated attacks with a moderate transaction size of up to CHF 70’000 are modelled by a compound Poisson process with beta marginal distribution.

Isolated attacks with transaction amounts larger than CHF 70’000 are modelled by a compound Poisson process with GPD marginal.

“Mass attacks” are modelled as a nested compound Poisson process, where the inner Poisson process simulates the individual transactions triggered by the mass attack. The inner process has a beta marginal distribution and generates transaction amounts up to CHF 20’000.

The intensities of the Poisson processes constituting the submodels vary. In our case, isolated attacks of moderate size were by far the most frequent, followed by isolated attacks of large size. Mass attacks were the least frequent.

Mobile banking fraud is modelled analogously, albeit with transaction sizes only up to CHF 20’000, because larger amounts were inadmissible on this channel during our investigation. Hence, there is no Poisson process with GPD marginal in this case. Contrastingly, in the EBICS channel, which is an internet-based payment channel between banks, only the possibility of large fraudulent transactions was of interest. Hence, this model consists of a single compound Poisson process with GPD marginals above CHF 100’000. The details of the parametrization are given in Appendix A .

Countermeasures against fraud and recovery measures after fraud events play an essential role in determining risk potential. Therefore, they were integrated into the risk models. Countermeasures against online fraud fall into two categories: those that strengthen general infrastructure of the payment process to make it harder for attackers to find a weak spot, and those that are geared towards fighting off actual attacks. The first type is conceptually part of the base model described above, as it affects the frequency and possibly the transaction size of attacks. However, the second type is better understood in the context of recovery.

A recovery variable is introduced in the triage model, which accounts for it often being possible to recover money even after it has been transferred to another bank through fraudulent transactions. Conversely, by monitoring transactions utilizing the fraud detection and triage model, a certain percentage of attacks can be identified even before the transactions are released. The ROC curve of the detection model’s ROC curve, in combination with the triage model, allows us to infer the probability of detection from the transaction size: such that this component of the recovery process is readily integrated into the stochastic framework.

Owing to the nonlinearity of the risk statistics, the aggregation of the models was performed at the level of individual scenarios. Thus, for each scenario, \(s_i\) , the loss of the overall model for one-year was calculated from the simulated loss events of the channel models:

For each sub-model, the loss is calculated by pulling the event frequency for the year according to the Poisson intensity, loss magnitude according to the marginal distribution, and stochastic recovery:

where \(\text {Rec}\) denotes recovery function. Simulated loss figures were obtained by simulating the nested overall model, from which the risk statistics could be calculated empirically. Juniper Research ( 2020 ) estimated the recovery rate as \(18\%\) .

The simulation results for online banking are presented in Table  6 . The table shows the simulation results without applying fraud detection utilizing a constant FPR level of 0.4% and the triage model for an integrated FPR of 0.4%, respectively. In this simulation, no additional recovery was applied.

The above table shows the strong mitigation of risk due to fraud detection. The triage model performs better than the constant FPR benchmark in all submodels, particularly for the GPD submodel. Recall that the triage model places strong emphasis on detecting large fraudulent transactions, even flagging all transactions larger than CHF \(192'000\) .

As a second application, we compare the results of this risk model for the three e-channels with the bank’s overall 2019 risk policy. This means that we compare the capital-at-risk (CaR) limits for market and credit risks with operational risk limits, where the e-channel part is now calculated in our model. The following allocation of CaR holds according to the annual report of the bank Footnote 1 : Credit Risk, 69%; operational risk, 11%; market risk trading, 4%; market risk treasury, 11%; market risk real estate, 2%; and investment, 4%.

Approximately 1% of operational risk capital can be attributed to these three channels. Even if we add another 4–5% of the total volume to all payment services, including corporate banking and interbank payments, less than 10% of the operational risk capital is attributed to payment systems. As payment systems account for a significant portion of operational risk, our results confirm serious doubts about the accuracy of the chosen operational risk capital in banks. Without reliable models and data, capital is determined by utilizing dubious business indicators. Our models, which represent a micro-foundation of risk, show that, at least in payment systems, trustworthy risk quantities can be derived by combining machine learning and statistics.

Defense against sophisticated online banking fraud involve several resources and methods. These include risk models, algorithms, human action, knowledge, computer tools, web technology, and online business systems in the context of risk management.

We show that anomaly detection is not only useful per se, identifying a significant proportion of fraud while controlling false alarms, but that linking anomaly detection with statistical risk management methods can significantly reduce risk. A bank equipped with an anomaly detection system will be exposed to orders of magnitude of higher risks in payments than a bank implementing our end-to-end risk management framework with the three components of fraud detection, fraud detection optimization, and risk modelling.

As fraud is part of regulated operational risk, our model allows us to analytically capture these operational risks without crude benchmarking. This also provides a microeconomic foundation for capital adequacy. In the area of operational risk, these results put internal models that are not risk sensitive or difficult to verify on a solid footing.

A complicated problem, such as online payment fraud detection, requires a comprehensive understanding. A prerequisite for this is access to a large dataset. To evaluate our method, we utilized a real dataset from a private bank. Regardless of the chosen algorithm, feature extraction is an essential part of developing an effective fraud detection method. We utilized historically observed and confirmed fraudulent transaction identifiers as the ground truth. Each feature in the feature vectors for each e-banking session aims to encode deviations from normal customer behavior. Thus, behavioral, transactional, and customer-specific features are important.

Our framework opens interesting directions for future research. Roughly speaking, the framework goes in only one direction, from machine learning methods in fraud detection to statistical risk modelling. The feedback process from the risk model to the triage model and from the triage model back to the fraud detection model is a challenging task that can be addressed utilizing reinforcement-learning methods. With such a feedback loop, the entire risk-management framework becomes a learning system. Another research direction is to extend the optimization of fraud detection (triage model) by considering transaction-dependent loss risks and other features such as customer segmentation. More emphasis is placed on segments that are known or suspected to be less alert or more vulnerable to fraudulent attacks. This resulted in a higher-dimensional triage model.

Availability of data and materials

The bank provided real transaction data and data on transactions (“raw data”) of the customers. The legal basis of the Swiss Federal Data Protection Act (2020) prevents the raw data from leaving the bank in any form or being accessible to any party other than the bank.

CaR for credit risk is VaR on the bank’s quantile level and for market risk CaR was in the past chosen on an annual basis and a risk budgeting process was defined to align present risk with the annual risk budget.

Abbreviations

Swiss Franc

Receiver operating curve

Area under the ROC curve

True positive rate

False poistive rate

Bagged decision tree

Local outlier factor

Isolation forest

Value-at-risk

Generalized pareto distribution

Payment channel for corporate banking clients

Extract, transform, load is a three-phase process where data is extracted, transformed and loaded into an output data container

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Acknowledgements

The authors are thankful to P. Senti, B. Zanella, A. Andreoli and R. Brun all from Zurich Cantonal Bank for the discussions and for providing us with the resources to perform his study. The authors are grateful to P. Embrechts (ETH Zurich) for the model discussions.

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

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Ermin Zvizdic

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Sebastiano Rossi and Ermin Zvizdic designed the fraud detection model and analysed the data. Thomas Domenig and Paolo Vanini designed the triage model. Thomas Domenig designed the risk model and did the calculations for the triage and risk model. Paolo Vanini wrote the manuscript with contributions from all authors. Paolo Vanini was involved in the analysis of the triage and risk model. Ermin Zvizdic led the project in the bank. All authors read and approved the final manuscript.

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The structure of our ensemble results from several design decisions based on both insights from the field (online banking) and experience in building machine learning models. Our ensemble model was built according to the following guidelines:

Customer-specific models The features used in our approach encode patterns in customers’ session behaviour and transactions. These patterns vary widely from client to client, which is why we chose to use a client-specific model rather than a global model.

Global feature space Although behaviours vary, we have chosen to use the same feature representation for each session/transaction, which allows us to assign weights to specific (model, feature) pairs based on their performance across all clients. This in turn allows for consistent scoring across all clients and information sharing between clients when fraudulent activity occurs. In other words, our approach makes it easy for learners to adjust their weights.

Separation of models based on feature type We have chosen to form separate ensembles, one based on behavioural features and one based on transactional features, rather than concatenating all features into a single vector and forming a single ensemble based on concatenation. This ensures better interpretability and reduces the likelihood of constructing nonsensical feature subspaces during feature bagging.

Modified Bootstrap aggregation (Bagging) To build an ensemble of weak learners, we use a modification of bootstrap aggregation (bagging). Bagging is a meta-algorithm for ensembles that is used to reduce the variance and improve the stability of the prediction as well as to avoid overfitting.

Bagging Pipeline

Observational sampling (bagging): Bagged ensembles for classification generate additional data for training by resampling with replacement from the initial training data to produce multiple sets of the same size of initial training data, one for each base learner. This is done to reduce the prediction variance. For the two outlier detection ensembles, we used variable subsampling (without replacement) to avoid problems associated with repeated data and to mimic random selection of the neighbourhood count hyperparameter (cf. Aggarwal and Sathe 2017 ).

Feature bagging: An important task in outlier detection is to identify the appropriate features on which to base the analysis. However, these features may differ depending on the fraud mechanism. Therefore, instead of pre-selecting features, a more robust approach is to create an ensemble of models that focus on different feature sets and assign different weights to the models that use different features depending on their performance. The procedure is applied to each base learner \(b_j\) as follows:

Randomly select a number \(r_{b_j }\) in range \([ d/10,d-1]\) , where d denotes the feature dimension.

Sample a subspace of features of size \(r_{b_j }\)

Train the base learner \(b_j\) on the sampled subspace.

No Hyperparameter bagging: Due to limited fraud, tuning the hyperparameters via validation may lead to overfitting. For this reason, similar to feature selection, we could instead randomly select a set of different hyperparameters. In our case, however, IF and BDT are not expected to be sensitive to the choice of hyperparameters, and resampling the hyperparameter from LOF would be redundant to the subsampling of the data performed. We therefore set all hyperparameters to reasonable ones found in the literature.

Sharing bagged features and parameters across customers : Subspaces for sampled features and parameters for local outlier factors are shared between all client models and all types of base models (manifested in the respective weak learners). This allows, for example, the introduction of supervision in the aggregation step and increased interpretability of the model, as it is easier to identify features relevant to the detection of certain types of fraud.

Model aggregation: Each base model provides a decision function \(\delta (x)\) for a given observation x . The base model ensemble directly aggregates (majority voting or averaging) the weak learner results based on different subsamples to form a single hypothesis that determines the class membership of an observation. Usually, this aggregation is directly extended after a normalisation step to include models of different types, parameters or feature groups. In our approach, however, this final aggregation is performed based on a monitoring step that uses knowledge of available frauds to assign different weights to each pair (model, feature set). Essentially, these weights quantify how appropriate each model and feature pair is for fraud detection.

We refer to a composite Poisson process whose marginal distribution corresponds to a beta distribution as a beta model or as a GPD model if the marginal distribution corresponds to a generalised Pareto distribution. The mass attack model is a nested compound Poisson process. The outer Poisson process models the mass attack event, while an inner Poisson process models the number of affected transactions. The extent of damage of the individual affected transactions is modelled with a beta distribution.

Online banking:

Beta model: Intensity 35, \(\alpha =0.42, \beta =2.4\) and \(\text {scale}: 71.000.\) By shifting and scaling, explicitly by the transformation \(x\rightarrow \alpha + (\beta -\alpha )x\) , the beta distribution is shifted from [0, 1] to the interval \([\alpha ,\beta ]\) . The parameter \(\alpha\) is called location, and \(\beta -\alpha\) scale.

GPD model: Intensity 3, \(\text {shape}=0.25, \text {location}=60.000, \text {scale}=100.000\) .

Mass Attack model: Intensity 0.1, intensity nested model 1000, Beta model \(\alpha =0.42, \beta =2.4\) and \(\text {scale}: 20.000.\)

Mobile banking:

Beta model: Intensity 45, \(\alpha =0.42, \beta =2.4\) and \(\text {scale}: 20'000.\)

Mass Attack model: Intensity 0.1, intensity nested model 1000, Beta model \(\alpha =0.42, \beta =2.4\) and \(\text {scale}: 20'000.\)

GPD model: \(\text {shape}=0.25, \text {location}=100'000, \text { scale}=300'000\) .

Recovery model:

The recovery model models the percentage recovery in a fraud case. It has the following form:

With a probability \(p_1=65\%\) , a complete recovery is simulated, i.e. no damage remains. This resulted from the fact that in the 159 fraud cases considered, it was actually possible to reduce the loss amount to zero even in \(80\%\) of the cases.

With a probability \(p_2=18\%\) , a recovery of zero is simulated, i.e. the damage corresponds to the full amount of the offence.

With probability \(1-p_1-p_2\) , a recovery between 0 and 1 is simulated. A beta distribution is chosen as the distribution of these partial recoveries.

The beta distribution parameters for the online banking channel were fitted on the fraud cases recorded from 13/03/2013 to 13/03/2018. These are 159 fraud cases, of which both the initial fraud transaction amounts and the effective loss amount, i.e. the residual amount after recovery, were recorded. Of the 159 cases, 152 have a fraud amount between CHF 0 and 60,000, while the remaining 7 fraud amounts range between CHF 100,000 and 300,000. A beta distribution was fitted on the 152 cases with fraud amounts up to 60,000 CHF, whereby the scaling parameter, i.e. the upper limit of the distribution, was defined as a free parameter of the fitting procedure and estimated by it to be 71,000 CHF. Similar procedures apply to the marginal distribution fits of the GPD and mass attack models.

There exists significant statistical uncertainty and variability in the driving forces of the defined models. Putting the intended flexibility of the model structure into practice, we distinguish between ’easily accessible’ parameters, which should be subject to discussion at any time in the context of risk assessments, and ’deeper’ parameters, whose mode of action is less obvious and whose adjustment is subject to the process of model reviews. Roughly speaking, Poisson intensities, which determine the expected frequency of events, as well as upper and lower boundaries of the marginal distributions belong to the former category, while shape parameters for the Beta and GPD marginal distributions belong to the latter.

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Vanini, P., Rossi, S., Zvizdic, E. et al. Online payment fraud: from anomaly detection to risk management. Financ Innov 9 , 66 (2023). https://doi.org/10.1186/s40854-023-00470-w

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DOI : https://doi.org/10.1186/s40854-023-00470-w

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  • Payment fraud risk management
  • Anomaly detection
  • Ensemble models
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research paper on banking frauds

A structured review and theme analysis of financial frauds in the banking industry

  • Published: 16 October 2020
  • Volume 9 , pages 305–321, ( 2020 )

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  • Pallavi Sood 1 , 2 &
  • Puneet Bhushan 1  

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Organizations of all types are vulnerable to frauds. Banks contribute to a significant extent in a country’s economic development by generating a large part of revenue in the service sector. Deterrence of fraud is impossible without understanding it. The present study attempts to extract themes by highlighting the major areas of the bank fraud literature within a specific time frame of 2000–2019 and finding the research gaps citing the future scope for research. Post the review of existing literature, using thematic analysis, two major themes were observed by the author, namely, “regulatory and compliance”–based studies and studies related to the “socio psychological” aspect of the literature. An examination of themes indicated that the popularity of the research areas shifted from accounting frauds or balance sheet frauds in early 2000s to areas such as cyber frauds and identity theft at the later decade. The present study paves way forward for future research on the bank customer’s vigilance, experiences and perspectives on frauds. Other significant areas of research include coping mechanisms with the various challenges faced by banks in marketing themselves after any major fraud occurrence.

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research paper on banking frauds

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Sood, P., Bhushan, P. A structured review and theme analysis of financial frauds in the banking industry. Asian J Bus Ethics 9 , 305–321 (2020). https://doi.org/10.1007/s13520-020-00111-w

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Received : 29 February 2020

Accepted : 28 September 2020

Published : 16 October 2020

Issue Date : December 2020

DOI : https://doi.org/10.1007/s13520-020-00111-w

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" A Study on The Impact of Online Banking Frauds: Customer's

Profile image of Kunga Dolma

The increasing technological changes it shows that world is changing at an increasing rate or staggering rate. Technology is being used as an important part of our life. Due to the acceptance of information technology at home and at workplaces many activities are being handled electronically. The concept of electronic banking is being popular among the customers of various financial institutions. It shows that customers are moving towards the internet banking to make their banking transactions easy and simple.

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The objective of this paper is to investigate the trends in frauds of computerised accounting information systems in Sri Lankan licensed banks. Sri Lankan licensed banking sector which is the most feasible service industry contributing enormously to the growth of the country. Security controls of Computerise accounting information system in the corporate sector is taken into consideration as this is currently a growing trend around the world. The core objective is to identify the frauds in the computerised accounting information systems in licensed banks in Sri Lanka. Critical reviews of past literature indicate a clear cut connection between fraud deductions in CAIS through security control systems. In order to find out the applicability of this relationship Sri Lanka, research will be undertaken in licence banks in Colombo region. The report consists secondary data which facilitate to achieve the aforementioned objectives. Secondary data analysis the existing computerized frauds in licensed banks as well as existing security control systems in licensed bank in Sri Lanka. However in secondary data analysis it clearly illustrate the computerized accounting frauds have been increased drastically. So licensed banks should have to implement the more security control systems to overcome those problems.

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Bassey I Ibor

The study investigated the phenomenon of banking sector frauds in Nigeria, staff involvement and the role of the human resource function in evolving curtailment strategies. It sought, particularly, to determine whether there is a relationship between the amounts of fraud loses in the banking sector and the cadre levels of employees. Using secondary data, estimated losses (EL) to fraud was regressed on number of fraud cases, total amounts and the categories of staff involved. The estimation of the model was based on the ordinary least squares (OLS) method after the necessary pre-testing of the annual time series data using the augmented Dickey-Fuller (ADF) and the Philips-Peron (PP) tests, taken side by side with the related descriptive statistics. The study revealed that the contribution of officers and accountants to fraud losses was the highest compared to the other categorized staff. The study model, considered good for prediction (DW=1.93), revealed that seventy seven percent of frauds are attributable to insiders and about 23% of fraud losses are attributable to non-insider related frauds and forgeries (R2=0.77). Based on the findings, it is recommended, inter alia, that the recruitment process be strengthened through a robust, IT-enabled selection, referencing and personnel lifestyle tracking system. Further, the work environment and job content must be enriched to curtail the opportunity to commit fraud presented when employees are allowed to have wholesome access to assets and information that allow them to both commit and conceal fraud.

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The need to reduce banks' operating costs and improve customer satisfaction has led to the deployment of Alternative Banking Channels (ABCs), through which banking transactions are performed. The Nigerian banking industry has aligned with this global trend leading to increased usage of ABCs by customers. However, fraudsters are taking advantage of this development to defraud unsuspecting customers resulting in customers' loss of confidence. Arising from this problem, this paper investigated the effect of card frauds on customers' confidence in ABCs in Nigeria. The study employed data obtained from Nigeria Deposit Insurance Corporation, Central Bank of Nigeria (CBN) and Nigerian Electronic Fraud Forum from 2012 to 2016. Various econometric analyses like Unit roots, co-integration test and Generalized Method of Moments were used to analyze the data. The Value of transactions on ABCs, proxied for customer confidence, was used as the dependent variable, while card frauds, proxied by value of frauds on Automated Teller Machines and Point of Sales (CPF), and web and online banking platforms (CNF) were included as independent variables. Banks' Total Assets, Prime Lending rate and Inflation rate were included as control variables. The results showed negative relationship between card frauds and customer confidence in ABCs. The paper concluded that card frauds affect customer confidence in ABCs negatively leading to customers preferring in-branch transactions. The paper recommended improved collaborations between banks and CBN to tackle frauds and leverage on the Bank Verification Number platform to improve security of transactions on ABCs through biometric authentication.

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Fraud trends spilling into early 2024

Account takeovers, phishing, deep fakes and other scams that flourished in 2023 will challenge bank security this year, too.

research paper on banking frauds

When it comes to fraud, what’s old is new again—sprinkled with a growing variety of fresh new scams, particularly as AI becomes more mainstream. Fraud experts discuss how 2023 compared with prior years and what’s on the horizon for this year and beyond.

“During the COVID pandemic, organized crime rings really gave banks a break, as they were very heavily focused on defrauding all the stimulus programs that were so poorly protected,” says Julie Conroy, chief insights officer at Datos Insights in Boston. “But as the stimulus program dried up, 2022 and 2023 have seen a strong resurgence of all the traditional types of fraud.”

Account takeover is up, as is application fraud, both significant concerns as more countries move to faster payments, Conroy says. While the industry thought check fraud was finally under control, it came back in 2023, says Ken Allen, an industry consultant based in Nashville, Tennessee. “The same thing happened with ACH fraud—we thought it was a little bit more under control with a lot more different technology options for moving money around, but good old ACH fraud was also on the rise in 2023,” Allen says.

Fraud trends continued to increase in 2023 as compared to prior years as technologies evolve, says Anna Kooi, national financial services leader at Wipfli LLP, who is based in Chicago. “Automation advancements have made it easier for criminals to hack accounts and remain undetected,” Kooi says. “Many times, software and/or bots are being used for tasks that previously required human intervention. This allows for increased frequency, and fraudsters can cover more ground than they could historically.”

Payments are getting faster, and so is fraud

Also on the rise: new opportunities for fraud resulting from new digital payment platforms that allow consumers and businesses to make quicker and more efficient payments, Kooi says. In the latest NICE Actimize Fraud Insights report comparing the first half of 2022 to the first half of 2023, globally there was a 22% increase in payment fraud as societies move from cash to cashless payments.

With that, there was an 18% increase in total attempted events, as well as an 18% increase in dollar volume, “which is significant,” says Rob Rendell, global head of fraud market strategy and fraud prevention for the Hoboken, New Jersey–based company.

“It’s not sustainable, and institutions need to take a more proactive stance curbing fraud,” Rendell says. In the U.S., there was a 33% increase in deposit fraud—fraudsters depositing checks into accounts that are later returned for fraudulent reasons, according to the report. In addition, fraudsters are securing mailbox keys by robbing letter carriers and then stealing checks out of the corresponding mailboxes.

“Fraudsters alter the checks and then deposit them into accounts under fictitious names, depleting the funds when available,” he says. “The Postal Service has announced they will slowly be converting from old locks to electronic locks to curb this issue.”

Check alterations are becoming increasingly more difficult to spot, especially after original checks are scanned and then destroyed, says Brandon Koeser, director and financial services industry senior analyst with RSM US LLP, who is based in Minneapolis. Phishing attempts also saw a meaningful increase in 2023, as fraudsters took advantage of generative AI to create more “convincing and compelling” text messages and emails, Koeser says.

On real-time payment rails, it’s very hard for institutions to detect unauthorized push payment fraud—fraudsters tricking people into voluntarily sending money out of their account and to the fraudsters, Conroy says. Fraudsters will then very quickly hop that money several times, so tracking it, finding it and calling it back is next to impossible.

“In the U.K., there is a liability shift that will go into effect in October of 2024 where for most forms of scam fraud, the consumer will not have liability, and there will be shared liability with the sending and receiving bank,” she says. “I think a lot of countries across the globe are taking a very close look at what the U.K. is doing, with an eye to potentially emulating this.”

What do experts expect in 2024 and beyond?

Looking ahead, what was successful in 2023 will still be successful going forward, Koeser says.

“Fraudsters are growing increasingly more adept at adjusting processes to maximize their success and ultimately their financial gain,” he says. “Check fraud, card-related fraud and phishing attempts aren’t going away; they are only growing more pronounced.”

First-party fraud will also be a challenge, as this type of fraud generally increases in tougher economic times, with inflation issues and interest rates “rising the way they are, whether we’re in a recession or not,” Allen says.

As part of this, there will likely be a continued increase in first-party fraud via people voluntarily acting as mules for criminal networks for a share of the proceeds, he says. For example, in “shop from home” scams, fraud networks recruit and dupe individuals into purchasing goods and sending them to another location where they will get to keep one item or a percentage of the goods purchased.

“In reality, the funds being used are either the individual being scammed into buying goods for the fraudster with their own funds, or they provide them stolen payment or prepaid credentials—and thus they become a part of the fraud,” Allen says. Then, there’s AI. “Generative AI, including ChatGPT, is fueling the fire for fraud,” Rendell says. “We’re anticipating deep fakes, voice cloning and manipulation of photos, as generative AI will be leveraged by fraudsters to carry out their attacks. The level of sophistication will only grow as fraudsters look to leverage these sophisticated tools.”

AI-powered fraud is going to have an impact in 2024, as ChatGPT and open AI become even more mainstream, Allen says. Fraudsters can emulate voices easily, so actions like creating fake IDs and emulating a person via video or voice to fool biometrics tools are going to continue. Meanwhile, managing cybersecurity issues can be tricky as financial institutions continue to improve their customer experience this year, Kooi says. Balancing “friction” and “fraud mitigation” becomes even more challenging as the number of systems increases. “Improving experience while reducing risk is top of mind for many CEOs today,” she says. “The move to decrease friction is increasing the risk around cybersecurity and is putting demand on the financial institutions’ talent.

Lack of talent is driving recent mergers of equals due to CEO retirements.” In 2024, institutions should keep a watchful eye on payment modernization, like the updates to ISO 20022 and what’s happening within the U.K. financial industry with the liability shift, Rendell says.

Institutions should then make sure that they have good controls in place related to scams, and that they will be able to recover lost funds should a liability shift happen here in the U.S. “That way, they’re not caught off guard and they’re not having to scramble to put controls in place to appease regulators—and they’ll have an understanding of what they could be on the hook for legally,” he says.

Having a sound fraud posture is key as the types and forms of fraud continue to grow, Koeser says. “Proactively educating your people and your clients to combat fraud is only one step,” he says. “But also communicating effectively when you become aware of something that could lead to fraud will be key to minimizing your financial loss while maintaining customer peace of mind.”

Financial institutions should also find more ways to work together to curb both existing and new types of fraud, Conroy says. “We all need to figure out how to invest in technologies that help us get faster, more nimble and more collaborative in fighting fraud. All of these attack vectors are only going to continue to escalate,” she says.

Katie Kuehner-Hebert is a contributing writer for BAI.

A version of this article first appeared in the BAI Executive Report  “Safeguarding Against Fraud.”  Read more on fraud and related topics.

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The Lasting Impacts of Scientific Fraud

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Far too many scientific papers are being retracted from prestigious scientific journals because scientists fabricated or falsified data. Although no one defends scientific fraud, few recognize its long-lasting impacts on governmental policy and society.

research paper on banking frauds

According to a 2022 study in the Netherlands, over the last three years, one in two researchers had engaged frequently in at least one “questionable research practice,” with “not submitting or resubmitting valid negative studies for publication” being the most common practice. The fields of life and medical sciences had the highest prevalence (55.3%) of engaging in questionable practices compared to other disciplines.        

According to the Office of Research Integrity, Department of Health and Human Services, scientific fraud or research misconduct involves :

Fabrication: making up data or results and recording or reporting them. Falsification: manipulating research materials, equipment, or processes, or changing or omitting data or results such that the research is not accurately represented in the research record. Plagiarism: the appropriation of another person's ideas, processes, results, or words without giving appropriate credit. Research misconduct does not include honest error or differences of opinion.

Scientific fraud can ripple across society with long-lasting consequences. Consider two: the first, Andrew Wakefield, vaccines and autism; the second, assessing cancer risk.

Vaccines and Autism

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Almost immediately, epidemiologic studies were performed and published, showing no causal link between autism and the MMR vaccine. However, the Wakefield study received worldwide publicity, and MMR vaccination rates began to drop over parental concern about the link between autism and the vaccine. 

It was not until 2010, twelve years later, that the interpretation of the original data was retracted by 10 of the 12 co-authors of the study.  The retraction said, "No causal link was established between MMR vaccine and autism as the data were insufficient.” Lancet also found that Andrew Wakefield had failed to notify them that he had been funded by lawyers representing parents in lawsuits against vaccine-producing companies. Although Wakefield lost his medical license, Lancet absolved Wakefield and his colleagues from charges of scientific misconduct or ethical violations.

In 2011, an investigative journalist wrote several articles on the MMR scare. After comparing the children’s anonymized case records with what was published in the Lancet article, he determined that the data had been substantially misrepresented to give Wakefield the desired result. Thus, the MMR scare was not poorly performed science; it was a deliberate fraud.

The Wakefield MMR vaccine article will likely go down as one of the most serious frauds in scientific history. This article fueled the anti-vax movement, and even today, 26 years after the publication of the retracted article, parents still refuse vaccinations due to the fear of autism.     

Cancer Risk Assessment

Cancer risk assessment is a critical feature of environmental health and regulatory policy used by governments to determine the environmental risks of chemicals and radiation. It answers the question as to what the risk is, at the population level, of developing cancer at different exposure levels.  

Dr. Ed Calabrese has spent the last 20 years researching the historical foundations of cancer risk assessment. His work culminated in a documentary by the Health Physics Society that presents the fraudulent science behind the linear no-threshold (LNT) dose-response model adapted by practically all U.S. agencies for chemical and radiation cancer risk assessment. 

  • The linear no-threshold (LNT) model assumes that every increment of a chemical or radiation dose, no matter how small, constitutes an increased cancer risk for humans. 
  • The threshold dose-response model assumes an exposure level below which there is no increased cancer risk.

Here are a few highlights from a long history of how we got to the current state of cancer risk assessment:

After World War II, the U.S. government was on a quest to better understand the biological impacts of radiation. Hermann Muller, a U.S. radiation geneticist, claimed to be the first to produce gene mutations using high exposure to X-rays in fruit flies, for which he received the Nobel Prize in 1946. However, Muller did not actually discover gene mutations. Instead, the X-rays used in his experiments caused holes in the chromosomes (not genetic mutations). Muller finally acknowledged significant errors in his research ten years after his Nobel Prize.

Muller used his prestige and power to make the case that there is no safe dose for radiation and that the long-standing use of the threshold dose-response model should be discarded and replaced with the LNT model and to block credible research that disagreed with his findings:

  • Ernest Caspari conducted the largest and best study of that time on the chronic effects of radiation on fruit flies (1946). The study showed that there was no increase in mutations at the “low” chronic radiation dose rate (still about 100,000 times above background). This study supported the threshold, not the LNT model. Mueller ignored Caspari’s results in his papers and presentations and convinced other professors not to accept Caspari’s findings.
  • The National Academy of Sciences created the Biological Effects of Atomic Radiation (BEAR) I Genetics Panel to advise the country on these issues. The Panel, under Muller’s direction, ignored any research that did not support “his” LNT model, including a 10-year study that showed no adverse effects on 75,000 offspring of the atomic bomb survivors of Hiroshima and Nagasaki. The Panel’s published findings in Science contained serious errors and misrepresentations, including altering the scientific record to mask the disagreements on risk assessment between the Panel members.
  • The Panel findings were adopted, and the LNT model became the regulatory standard for the EPA. The descendant of BEAR, the Biological Effects of Ionizing Radiation (BEIR) Committee, has issued many reports since then, clinging fervently to the LNT model despite all contrary scientific evidence.

The impact of the LNT model guiding Federal Agencies is that every questionable molecule of a chemical or radiation must be eliminated, or public health is at risk. This has resulted in billions of dollars spent to eliminate chemicals to levels that have no impact on human health and the unnecessary removal of products, many of which are critical today.

View the discussion thread.

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By Susan Goldhaber MPH

Susan Goldhaber, M.P.H., is an environmental toxicologist with over 40 years’ experience working at   Federal and State agencies and in the private sector, emphasizing issues concerning chemicals in drinking water, air, and hazardous waste.  Her current focus is on translating scientific data into usable information for the public. 

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A research-technology firm has developed a new approach to help identify journal articles that originate from paper mills — companies that churn out fake or poor-quality studies and sell authorships.

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Read the full civil fraud trial ruling imposing penalty of over $350 million against Trump

NEW YORK (AP) — A New York judge ruled Friday against Donald Trump, imposing a $364 million penalty over what the judge ruled was a yearslong scheme to dupe banks and others with financial statements that inflated the former president’s wealth.

Trump also was barred from serving as an officer or director of any New York corporation for three years.

Judge Arthur Engoron issued his decision after a 2½-month trial that saw the Republican presidential front-runner bristling under oath that he was the victim of a rigged legal system.

The stiff penalty was a victory for New York Attorney General Letitia James, a Democrat, who sued Trump over what she said was not just harmless bragging but years of deceptive practices as he built the multinational collection of skyscrapers, golf courses and other properties that catapulted him to wealth, fame and the White House.

Trump’s lawyers had said even before the verdict that they would appeal.

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N.Y. judge denies Trump request to delay hush money trial

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Trump’s Harsh Punishment Was Made Possible by This New York Law

The little-known measure meant hundreds of millions in penalties in the civil fraud case brought by Attorney General Letitia James.

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Letitia James sits in court behind Donald Trump, who is blurred and out of focus.

By Ben Protess and Jonah E. Bromwich

The $355 million penalty that a New York judge ordered Donald J. Trump to pay in his civil fraud trial might seem steep in a case with no victim calling for redress and no star witness pointing the finger at Mr. Trump. But a little-known 70-year-old state law made the punishment possible.

The law, often referred to by its shorthand, 63(12), which stems from its place in New York’s rule book, is a regulatory bazooka for the state’s attorney general, Letitia James. Her office has used it to aim at a wide range of corporate giants: the oil company Exxon Mobil, the tobacco brand Juul and the pharma executive Martin Shkreli.

On Friday, the law enabled Ms. James to win an enormous victory against Mr. Trump. Along with the financial penalty , the judge barred Mr. Trump from running a business in New York for three years. His adult sons were barred for two years.

The judge also ordered a monitor, Barbara Jones, to assume more power over Mr. Trump’s company, and asked her to appoint an independent executive to report to her from within the company.

A lawyer for Mr. Trump, Christopher M. Kise, reacted with fury, saying “the sobering future consequences of this tyrannical abuse of power do not just impact President Trump.”

“When a court willingly allows a reckless government official to meddle in the lawful, private and profitable affairs of any citizen based on political bias, America’s economic prosperity and way of life are at extreme risk of extinction,” he said.

In the Trump case, Ms. James accused the former president of inflating his net worth to obtain favorable loans and other financial benefits. Mr. Trump, she argued, defrauded his lenders and in doing so, undermined the integrity of New York’s business world.

Mr. Trump’s conduct “distorts the market,” Kevin Wallace, a lawyer for Ms. James’s office, said during closing arguments in the civil fraud trial.

“It prices out honest borrowers and can lead to more catastrophic results,” Mr. Wallace said, adding, “That’s why it’s important for the court to take the steps to protect the marketplace to prevent this from happening again.”

Yet the victims — the bankers who lent to Mr. Trump — testified that they were thrilled to have him as a client. And while a parade of witnesses echoed Ms. James’s claim that the former president’s annual financial statements were works of fiction, none offered evidence showing that Mr. Trump explicitly intended to fool the banks.

That might seem unusual, but under 63(12), such evidence was not necessary to find fraud.

The law did not require the attorney general to show that Mr. Trump had intended to defraud anyone or that his actions resulted in financial loss.

“This law packs a wallop,” said Steven M. Cohen, a former federal prosecutor and top official in the attorney general’s office, noting that it did not require the attorney general to show that anyone had been harmed.

With that low bar, Justice Arthur F. Engoron, the judge presiding over the case, sided with Ms. James on her core claim before the trial began, finding that Mr. Trump had engaged in a pattern of fraud by exaggerating the value of his assets in statements filed to his lenders.

Ms. James’s burden of proof at the trial was higher: To persuade the judge that Mr. Trump had violated other state laws, she had to convince him that the former president acted with intent. And some of the evidence helped her cause: Two of Mr. Trump’s former employees testified that he had final sign-off on the financial statements, and Mr. Trump admitted on the witness stand that he had a role in drafting them.

Still, her ability to extract further punishments based on those other violations is also a product of 63(12), which grants the attorney general the right to pursue those who engage in “repeated fraudulent or illegal acts.”

In other fraud cases, authorities must persuade a judge or jury that someone was in fact defrauded. But 63(12) required Ms. James only to show that conduct was deceptive or created “an atmosphere conducive to fraud.” Past cases suggest that the word “fraud” itself is effectively a synonym for dishonest conduct, the attorney general argued in her lawsuit.

Once the attorney general has convinced a judge or jury that a defendant has acted deceptively, the punishment can be severe. The law allows Ms. James to seek the forfeit of money obtained through fraud.

Of the roughly $355 million that Mr. Trump was ordered to pay, $168 million represents the sum that Mr. Trump saved on loans by inflating his worth, she argued. In other words, the extra interest the lenders missed.

The penalty was in the judge’s hands — there was no jury — and 63(12) gave him wide discretion.

The law also empowered Justice Engoron to set new restrictions on Mr. Trump and his family business, all of which Mr. Trump is expected to appeal.

The judge also ordered a monitor to assume more power over Mr. Trump’s company, who will appoint an independent executive who will report to the monitor from within the company.

Even before she filed her lawsuit against the Trumps in 2022, Ms. James used 63(12) as a cudgel to aid her investigation.

The law grants the attorney general’s office something akin to prosecutorial investigative power. In most civil cases, a person or entity planning to sue cannot collect documents or conduct interviews until after the lawsuit is filed. But 63(12) allows the attorney general to do a substantive investigation before deciding whether to sue, settle or abandon a case. In the case against Mr. Trump, the investigation proceeded for nearly three years before a lawsuit was filed.

The case is not Mr. Trump’s first brush with 63(12). Ms. James’s predecessors used it in actions against Trump University, his for-profit education venture, which paid millions of dollars to resolve the case.

The law became so important to Ms. James’s civil fraud case that it caught the attention of Mr. Trump, who lamented the sweeping authority it afforded the attorney general and falsely claimed that her office rarely used it.

He wrote on social media last year that 63(12) was “VERY UNFAIR.”

William K. Rashbaum contributed reporting.

Ben Protess is an investigative reporter at The Times, writing about public corruption. He has been covering the various criminal investigations into former President Trump and his allies. More about Ben Protess

Jonah E. Bromwich covers criminal justice in New York, with a focus on the Manhattan district attorney's office, state criminal courts in Manhattan and New York City's jails. More about Jonah E. Bromwich

IMAGES

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  1. (PDF) Banking Frauds: Causes and Preventions

    Banking fraud refers to an illegal act done with the intention to gaining financial advantage from financial institutions. Mitigating banking fraud is possible but its eradication is not. A...

  2. A systematic literature review on frauds in banking sector

    The purpose of this paper is to systematically review the literature published in past 20 years on bank frauds and present a holistic view on causes and consequences of bank frauds and measures to curtail this menace. Towards the end the paper provides avenues for future research.

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  5. A structured review and theme analysis of financial frauds in the

    The present study paves way forward for future research on the bank customer's vigilance, experiences and perspectives on frauds. Other significant areas of research include coping mechanisms with the various challenges faced by banks in marketing themselves after any major fraud occurrence. Similar content being viewed by others

  6. Online payment fraud: from anomaly detection to risk management

    Fraud arises in the financial industry via numerous channels, such as credit cards, e-commerce, phone banking, checks, and online banking. Juniper Research reports that e-commerce, airline ticketing, money transfer, and banking services will cumulatively lose over $ 200 billion due to online payment fraud between 2020 and 2024.The increased sophistication of fraud attempts and the increasing ...

  7. Intelligent financial fraud detection practices in post-pandemic era

    Introduction. Over the past decades, financial fraud has brought shocking losses to the global economy, threatening the efficiency and stability of capital markets. 1, 2 Making things worse, the coronavirus pandemic (COVID-19) outbreak in early 2020 disrupted the international financial markets in unprecedented ways, heightening the risk of being vulnerable to financial fraud. 3 For example ...

  8. Full article: Financial fraud, scandals, and regulation: A conceptual

    2. Reviewed historical studies, method, and design. The publications reviewed in this article were selected by means of a query in Proquest, which is the most comprehensive database of publications in English language business history journals. 2 The query included both articles and - in order to trace book publications - book reviews. The search keys used were 'scandal*', 'fraud ...

  9. Prevention and Control of Financial Fraud: a Scoping Review

    According to several scholars (e.g., Albanese, 2005), fraud is the characteristic crime of the twenty-first century, in the same way that larceny characterised much of the twentieth century.With its numerous online and offline versions, fraud rates —in both police figures and victimisation surveys — have been growing, offsetting the almost generalised crime drop that has been going on in ...

  10. PDF A structured review and theme analysis of financial frauds in the

    This paper explores the phenomenon of fraud in the context of banking operations. Financial fraud has clearly become a widespread obstruction in the growth of the global banking industry. The aftermath of many significant reports published on the banking fraud landscape revealed a great number of scandals.

  11. Measuring Fraud in Banking and Its Impact on the Economy: A ...

    Abstract. This paper suggests a novel approach to measuring fraud in banking and to evaluating its cross-sectional and aggregate implications. I explore unique evidence of declining regulatory forbearance from the Russian banking system in the 2010s, when the central bank forcibly closed roughly two-thirds of all operating banks for fraudulent activities.

  12. Financial Fraud Detection Based on Machine Learning: A Systematic

    It can be shown that out of the review papers, 50% articles focus on bank fraud, 29% addressed financial statements, and 21% focused on insurance fraud and other fraud at 14% and 7%, respectively. ... Z. Research on Bank Anti-Fraud Model Based on K-Means and Hidden Markov Model. In Proceedings of the 2018 IEEE 3rd International Conference on ...

  13. Financial Fraud: A Review of Anomaly Detection ...

    Bolton and Hand, authors of some of the earliest and most influential surveys of statistical fraud detection, provide an in-depth background on the various types of financial fraud and how they are committed, such as credit card fraud, insurance fraud, money laundering, and others ( Bolton & Hand, 2002Kou, Lu, Sirwongwattana, & Huang, 2004Phua, ...

  14. An Empirical Study of Frauds in the Banks

    In 2015, the RBI has introduced new mechanisms for banks to check loan frauds by taking pro-active steps by setting up a Central Fraud Registry, introduced the concept of Red Flagged Account, and Indian investigative agencies (CBI, CEIB) will soon start sharing their databases with banks.

  15. " A Study on The Impact of Online Banking Frauds: Customer's

    This paper examines internet banking fraud awareness by the banking sector in Southern Africa as financial consum-ers' protection. The study is grounded on routine activity theory and criminology theory.

  16. PDF A Study on Causes and Prevention of Fraud in Banking Industry

    Fraud is a social evil it affect the entire sector of the economy which banking system is inclusive although fraud and loss in an expected part of any business activity the scope and gravity is what creates concern in a developing economy like ours.

  17. Impacts of Cyber Crime on Internet Banking

    The research in this paper critically analyzes and discusses the effects of cyber threats when dealing with online banking services. It is concluded that by the research that there is a need to increase safety measures in available cyber crimes when dealing with Internet banking and sensitive financial data.

  18. PDF Frauds in Banking Sector in India: Analysis and Preventive Measures

    This paper aims at studying the number of frauds being committed bank wise, sector wise and its impact on the performance of the banks. A study was conducted on the impact of frauds on Punjab National bank. This study is based on secondary data collected from various reliable sources.

  19. PDF Banking Frauds in India; A case analysis

    In this paper, the researchers analyzed various bank frauds registered with CBI and convicted by CBI court (2015 to 2017). The researchers depicts the various types of frauds in the Indian scenario and also revealed its legal action taken by the investigation agency.

  20. PDF A Study of Electronic Banking Fraud, Fraud Detection and Control

    This paper critically examines electronic banking frauds, detection of electronic banking frauds, ... commit banking frauds is termed electronic banking fraud. Ref "[13]" defined electronic banking frauds as frauds ... mitigate the fraud. This research work reviewed two of such technologies. The use of artificial neural network and geographic

  21. Analysis and Preventive Measures of Banking Frauds with Reference to

    Analysis and Preventive Measures of Banking Frauds with Reference to Indian Banking Sector. February 2024. DOI: 10.2991/978-94-6463-374-0_34. CC BY-NC 4.0. In book: Proceedings of the 3rd ...

  22. Analysis and Preventive Measures of Banking Frauds with Reference to

    The study revealed that group wise and operation wise number of bank frauds differed significantly. This paper idea will help policy makers to reduce the degree of future frauds. ... Business and Management Research Publication Date 20 February 2024 ... 2024 DA - 2024/02/20 TI - Analysis and Preventive Measures of Banking Frauds with Reference ...

  23. Fraud trends spilling into early 2024

    Fraud trends spilling into early 2024. Account takeovers, phishing, deep fakes and other scams that flourished in 2023 will challenge bank security this year, too. Feb 15, 2024 / Fraud Prevention. When it comes to fraud, what's old is new again—sprinkled with a growing variety of fresh new scams, particularly as AI becomes more mainstream.

  24. PDF 2024 National Money Laundering Risk Assessment (NMLRA)

    prison-bust-out-bank-fraud-scheme#:~:text= percentE2 percent80 percent94 percent20Kyung percent20Min percent20Kong percent2C percent2055 percent2C,Talbert percent20announced; DOJ, "Colorado Man Pleads Guilty to "Bust Out" Bank Fraud

  25. The Lasting Impacts of Scientific Fraud

    The Lasting Impacts of Scientific Fraud. By Susan Goldhaber MPH — February 13, 2024. Far too many scientific papers are being retracted from prestigious scientific journals because scientists fabricated or falsified data. Although no one defends scientific fraud, few recognize its long-lasting impacts on governmental policy and society.

  26. Fake research papers flagged by analysing authorship trends

    A research-technology firm has developed a new approach to help identify journal articles that originate from paper mills — companies that churn out fake or poor-quality studies and sell ...

  27. Capital One to buy Discover Financial: What to watch

    The big picture: Consumers with poor credit who have a credit card from a small bank are paying a lower interest rate on their purchases (20.6%, on average) than consumers with excellent credit who carry a card from Discover, Capital One, or one of the other big banks. They're paying 23%, on average. Credit card interest rates have risen ...

  28. Read the full civil fraud trial ruling imposing over $350 million

    NEW YORK (AP) — A New York judge ruled Friday against Donald Trump, imposing a $364 million penalty over what the judge ruled was a yearslong scheme to dupe banks and others with financial ...

  29. Trump's Civil Fraud Trial Penalty Was Made Possible by This New York

    Feb. 16, 2024 Updated 9:59 a.m. ET. When a New York judge delivers a final ruling in Donald J. Trump's civil fraud trial as soon as Friday, the former president could face hundreds of millions ...