Banking Fraud Detection Research Articles

COMPARATIVE ANALYSIS OF MACHINE LEARNING ALGORITHMS FOR BANKING FRAUD DETECTION: A STUDY ON PERFORMANCE, PRECISION, AND REAL-TIME APPLICATION

COMPARATIVE ANALYSIS OF MACHINE LEARNING ALGORITHMS FOR BANKING FRAUD DETECTION: A STUDY ON PERFORMANCE, PRECISION, AND REAL-TIME APPLICATION

An Investigation of Machine Learning Applications in the Financial Fraud Detection

Financial fraud presents itself in various forms, and it often involves intricate financial transaction networks, making it challenging to detect the perpetrators and identify the characteristics of the fraud. In recent years, machine learning has gained widespread application within the financial sector. Therefore, various financial fraud detection models have been developed based on diverse machine learning methodologies. In the method part, this paper provides an overview of the machine learning process and then discusses the application of machine learning models in various financial fraud scenarios. Financial fraud in the insurance field has been further refined into automobile and medical insurance fraud. In automobile insurance fraud detection, some studies applied implicit Naive Bayes Model to analyze observable features and estimate hidden variables, and some studies used resampler to solve data imbalance and adopted 7 kinds of machine learning models for analysis. In health insurance fraud detection, many studies train medical data with multiple models, including AdaBoost, Logistic Regression and Support Vector Machine. In credit card fraud detection, studies use five algorithms, including random forest and decision tree et al., and some construct a model combining Decision Tree (DT) and Logistic Regression (LR). In bank fraud detection, some studies introduce Value-at-Risk to combine it with machine learning algorithms, and some studies propose a decentralized model training method based on federated learning. In the discussion section, this paper addresses the current limitations of the research, such as poor interpretability, uneven distribution of data sets, and issues related to customer privacy, and proposes corresponding solutions.

Online banking fraud detection: A comparative study of cases from South Africa and Spain

Background: The banking sector provides online banking to offer their customers convenient and easy access to banking services. As most banking services are nowadays performed online, alarming fraudulent activities occur daily. The online banking frauds are increasingly and commonly being experienced globally and are damaging to both banks and customers.Objectives: The goal of the study was to investigate issues in online fraud detection in the banking sector from South Africa and Spain.Method: The study followed the Design Science Research (DSR) methodology. Data were collected through the qualitative approach particularly using focus groups and semi-structured interviews. The study followed non-probability sampling since it focussed on a specialised area of online fraud model. In total, the population size consisted of 17 participants, which included fraud technical managers, fraud investigator specialists, fraud technical specialists and data scientists.Results: In the South African context, the study established that there is a lack of online fraud experts in the banking sector. The findings reveal that the lack of online fraud expertise may lead to banks having weak detection systems. In the Spanish perspective, the study revealed that there is a lack of law regulation that poses a high risk.Conclusion: Given the lack of online fraud experts in the banking sector, banks are urged to acquire and develop online fraud expertise. Online banking technology is developing expeditiously than traditional transactions; therefore, regulations and policies need to be updated regularly to keep up with rapidly evolving technological changes.Contribution: Considering the significance of global online banking, the study suggested areas that the banking sector may investigate to develop and enhance online fraud detection models to combat online fraudulent activities.

Real-Time Online Banking Fraud Detection Model by Unsupervised Learning Fusion

Digital trades and payments are becoming increasingly popular, as they typically entail monetary transactions. This not only makes electronic transactions more convenient for the end customer, but it also raises the likelihood of fraud. An adequate fraud detection system with a cutting-edge model is critical to minimizing fraud costs. Identifying fraud at the ideal time entails establishing and setting up ubiquitous systems to consume and analyze massive amounts of streaming data. Recent advances in data analytics methods and introducing open-source technology for big data storage and processing opened new options for detecting fraud. This study aims to tackle this critical issue by providing a newly real-time e-transaction fraud detection schema that consolidates the advantages of both unsupervised learners, including autoencoder and extended isolation forests, with cutting-edge big data gadgets such as Spark streaming and sparkling water. It addresses the shortage of non-fraudulent instances and handles the excessive dimension of the set of features. On two real-world transactional datasets, we assess our suggested technique. Compared with other current fraud identification systems, our methodology delivers an elevated accuracy yield of 99%. Furthermore, it outperforms state-of-the-art approaches in reliably identifying fraudulent samples. Doi: 10.28991/HIJ-2024-05-01-014 Full Text: PDF

A Sequential Pattern Mining Method for the Individualized Detection of Online Banking Fraudulent Transactions

Financial institutions are facing a growing number of challenges as a result of the growth in fraudulent activities in online banking, which calls for the implementation of systems that are more advanced in their identification of fraud.Traditional rule-based techniques have difficulties when it comes to handling rule modifications and do not provide sufficient detail to particular users. This study presents the User-Specific Sequence Pattern Mining Framework for Online Banking Fraud Detection (USPM-FrauD) as a solution to the challenges that have been highlighted. The framework was developed in order to detect fraudulent activity in online banking.This system makes use of sequence pattern mining algorithms in order to examine the transaction logs of every user and find variations that may be indicative of fraudulent conduct. By concentrating on individualised transaction patterns, the USPM-FrauD model provides a method that is both more efficient and more specifically targeted to the identification of fraudulent activity in online banking. When compared to rule-based and Markov chain models, the experimental findings reveal that the USPM-FrauD model has greater performance. This demonstrates that the model has the potential to be practically implemented in online banking systems that are used in the real world. In order to battle the ever-changing world of online banking fraud, financial institutions now have access to a more complex and user-specific tool thanks to the USPM-FrauD framework, which marks a substantial improvement in fraud detection approaches.

Neuro-Fuzzy Models for Electronic Banking Fraud Detection and Prevention: A Review of Recent Advances

Online payments have evolved over the years. Today, more people are choosing electronic payments platforms over method traditional banking. From POS, mobile banking, to virtual banking services, there are lots of trends to facilitate seamless payments for customers. However, electronic fraud is affecting Nigeria’s financial system, costing the economy dearly and holding back the adoption of cashless technologies due to rise in fraudulent activities. This work focused on informing and helping the public to understand payment fraud issues occurring on various channels, as well as aid financial institutions with more effective fraud monitoring and preventive measures to combat the fraud. In this study, five feature of electronic banking transaction were selected and used to obtain universe of discourse, membership functions and their linguistic variables for the fuzzy system and are classified based on ANFIS to allow an E-banking fraud detection system to test and classify transactions as fraudulent or safe.

Efficient Bank Fraud Detection with Machine Learning

Efficient Bank Fraud Detection with Machine Learning

Detecting banking frauds with analytics and machine learning

Bank fraud is the bodily loss of a Bank or maybe the loss of very sensitive info. For detection, there are lots of machine learning algorithms which can be used. The study shows many algorithms which could be used for deciding transactions as fraud or perhaps real. The information set employed in Bank fraud Detection was utilized in the research. The SMOTE method was used for oversampling, since the dataset was incredibly imbalanced. Moreover, include choice was performed, and the set was divided into two parts, test data and instruction information. The algorithms used in this study were Logistic Regression, Multilayer Perceptron, Random Forest and Naive Bayes. The results show that every algorithm could be used with good precision for fraud detection of banking solutions. For the detection of extra constipation, the proposed model might be used.

Bank Fraud Detection with Graph Neural Networks

Bank Fraud Detection with Graph Neural Networks

Adam in Private: Secure and Fast Training of Deep Neural Networks with Adaptive Moment Estimation

Machine Learning (ML) algorithms, especially deep neural networks (DNN), have proven themselves to be extremely useful tools for data analysis, and are increasingly being deployed in systems operating on sensitive data, such as recommendation systems, banking fraud detection, and healthcare systems. This underscores the need for privacy-preserving ML (PPML) systems, and has inspired a line of research into how such systems can be constructed efficiently. However, most prior works on PPML achieve efficiency by requiring advanced ML algorithms to be simplified or substituted with approximated variants that are “MPC-friendly” before multi-party computation (MPC) techniques are applied to obtain a PPML systems. A drawback of this approach is that it requires careful fine-tuning of the combined ML and MPC algorithms, and might lead to less efficient algorithms or inferior quality ML (such as lower prediction accuracy). This is an issue for secure training of DNNs in particular, as this involves several arithmetic algorithms that are thought to be “MPCunfriendly”, namely, integer division, exponentiation, inversion, and square root extraction. In this work, we take a structurally different approach and propose a framework that allows efficient and secure evaluation of full-fledged state-of-the-art ML algorithms via secure multi-party computation. Specifically, we propose secure and efficient protocols for the above seemingly MPC-unfriendly computations (but which are essential to DNN). Our protocols are three-party protocols in the honest-majority setting, and we propose both passively secure and actively secure with abort variants. A notable feature of our protocols is that they simultaneously provide high accuracy and efficiency. This framework enables us to efficiently and securely compute modern ML algorithms such as Adam (Adaptive moment estimation) and the softmax function “as is”, without resorting to approximations. As a result, we obtain secure DNN training that outperforms state-of-the-art threeparty systems; our full training is up to 6.7 times faster than just the online phase of FALCON (Wagh et al. at PETS’21) and up to 4.2 times faster than Dalskov et al. (USENIX’21) on the standard benchmark network for secure training of DNNs. The potential advantage of our approach is even greater when considering more complex realistic networks. To demonstrate this, we perform measurements on real-world DNNs, AlexNet and VGG16, which are large networks containing millions of parameters. The performance of our framework for these networks is up to a factor of 26 ∼ 33 faster for AlexNet and 48 ∼ 51 faster for VGG16 to achieve an accuracy of 60% and 70%, respectively, when compared to FALCON. Even compared to CRYPTGPU (Tan et al. IEEE S&P’21), which is optimized for and runs on powerful GPUs, our framework achieves a factor of 2.1 and 4.1 faster performance, respectively, on these networks.

Emerging Needs of Forensic Audit in Corporate and Banking Frauds in India

“When we discuss about good corporate governance we always have to address the matter of frauds which acts as an obstacle for comprehensive growth and development. Corporate and banking frauds are very significant and rampant issue as they have irreversible and traumatic impact on stakeholders and reputational impact on corporate and banks. Thus there is an emerging need to cope up with this menace and forensic audit is considered as a powerful weapon which is used for corporate and banking fraud detection, mitigation and prevention. Forensic audit is comparatively a new term for a country like India, but with growing number of white collar crime, forensic audit professionals are in considerable demand. This paper explains the concept of corporate and banking fraud in India and role of forensic audit as a Brahmastra (Brahma weapons) against this economic devil. Relevant Secondary data from various publications has been reviewed and synthesized for this research paper”

Empirical Convergence Theory of Harmony Search Algorithm for Box-Constrained Discrete Optimization of Convex Function

The harmony search (HS) algorithm is an evolutionary computation technique, which was inspired by music improvisation. So far, it has been applied to various scientific and engineering optimization problems including project scheduling, structural design, energy system operation, car lane detection, ecological conservation, model parameter calibration, portfolio management, banking fraud detection, law enforcement, disease spread modeling, cancer detection, astronomical observation, music composition, fine art appreciation, and sudoku puzzle solving. While there are many application-oriented papers, only few papers exist on how HS performs for finding optimal solutions. Thus, this preliminary study proposes a new approach to show how HS converges on an optimal solution under specific conditions. Here, we introduce a distance concept and prove the convergence based on the empirical probability. Moreover, a numerical example is provided to easily explain the theorem.

ReMEMBeR: Ranking Metric Embedding-Based Multicontextual Behavior Profiling for Online Banking Fraud Detection

Anomaly detection relies on individuals' behavior profiling and works by detecting any deviation from the norm. When used for online banking fraud detection, however, it mainly suffers from three disadvantages. First, for an individual, the historical behavior data are often too limited to profile his/her behavior pattern. Second, due to the heterogeneous nature of transaction data, there lacks a uniform treatment of different kinds of attribute values, which becomes a potential barrier for model development and further usage. Third, the transaction data are highly skewed, and it becomes a challenge to utilize the label information effectively. The three disadvantages result in both poor generalization and high false positive rate of anomaly detection, and we propose a ranking metric embedding based multi-contextual behavior profiling (ReMEMBeR) model to battle them effectively. We solve the original fraud detection problem as a pseudo-recommender system problem, where an individual is treated as a pseudo-user, his/her behavior as a pseudo-item, and the label as the corresponding pseudo-rating. With the idea of collaborative filtering, for an individual, information from other similar individuals can be used to establish his/her behavior profile. In order to obtain a uniform treatment of heterogeneous attributes, we turn to an embedding based method to learn both attribute embedding and individuals' behavior profiles within a common latent space simultaneously. To utilize the label information better, our model is designed to fit pseudo-users' correct preference ranking for pseudo-items. By doing so, it explicitly learns to tell the fraudulent from the legitimate. Last but not least, we propose to identify and distinguish individuals under different contexts and further generalize the behavior profiling model to be a multi-contextual one. The proposed model can, thus, integrate the multi-contextual behavior patterns and allow transactions to be examined under the different contexts. Extensive experiments on a real-world online banking transaction dataset demonstrate that our model not only outperforms benchmarks on all metrics but also can be combined with them to achieve even better performance.

New Feature Engineering Framework for Deep Learning in Financial Fraud Detection

The total losses through online banking in the United Kingdom have increased because fraudulent techniques have progressed and used advanced technology. Using the history transaction data is the limit for discovering various patterns of fraudsters. Autoencoder has a high possibility to discover fraudulent action without considering the unbalanced fraud class data. Although the autoencoder model uses only the majority class data, in our hypothesis, if the original data itself has various feature vectors related to transactions before inputting the data in autoencoder then the performance of the detection model is improved. A new feature engineering framework is built that can create and select effective features for deep learning in remote banking fraud detection. Based on our proposed framework [19], new features have been created using feature engineering methods that select effective features based on their importance. In the experiment, a real-life transaction dataset has been used which was provided by a private bank in Europe and built autoencoder models with three different types of datasets: With original data, with created features and with selected effective features. We also adjusted the threshold values (1 and 4) in the autoencoder and evaluated them with the different types of datasets. The result demonstrates that using the new framework the deep learning models with the selected features are significantly improved than the ones with original data.

A Combination of Mathematics, Statistics and Machine Learning To Detect Fraud

Fraud detection which is a discussible phenomenon to many bounds together with financial sectors, banking, insurance as well as diverse forms of industries. Nowadays fraud endeavors are amplifying with rampant pace especially via the development of technology, so building fraud discovery more significant than ever before. In this paper, we analyzed the fraud operations by managing the “Banking Fraud Detection” database by the combination of mathematical, statistical as well as machine learning ways and tried to spectacle a comparison among these ways. We narrated the elementary explorations by the combining methods such as mathematical, statistical as well as machine learning to equip the ways of insidious banking transactions.

Learning maximum excluding ellipsoids from imbalanced data with theoretical guarantees

In this paper, we address the problem of learning from imbalanced data. We consider the scenario where the number of negative examples is much larger than the number of positive ones. We propose a theoretically-founded method which learns a set of local ellipsoids centered at the minority class examples while excluding the negative examples of the majority class. We address this task from a Mahalanobis-like metric learning point of view and we derive generalization guarantees on the learned metric using the uniform stability framework. Our experimental evaluation on classic benchmarks and on a proprietary dataset in bank fraud detection shows the effectiveness of our approach, particularly when the imbalancy is huge.

A survey of game theoretic approach for adversarial machine learning

The field of machine learning is progressing at a faster pace than ever before. Many organizations leverage machine learning tools to extract useful information from a massive amount of data. In particular, machine learning finds its application in cybersecurity that begins to enter the age of automation. However, machine learning applications in cybersecurity face unique challenges other domains rarely do—attacks from active adversaries. Problems in areas such as intrusion detection, banking fraud detection, spam filtering, and malware detection have to face challenges of adversarial attacks that modify data so that malicious instances would evade detection by the learning systems. The adversarial learning problem naturally resembles a game between the learning system and the adversary. In such a game, both players would attempt to play their best strategies against each other while maximizing their own payoffs. To solve the game, each player would search for an optimal strategy against the opponent based on the prediction of the opponent's strategy choice. The problem becomes even more complicated in settings where the learning system may have to deal with many adversaries of unknown types. Applying game‐theoretic approach, robust learning techniques have been developed to specifically address adversarial attacks and the preliminary results are promising. In this review, we summarize these results.This article is categorized under:Technologies > Machine LearningFundamental Concepts of Data and Knowledge > Key Design Issues in Data Mining

Big Data Business Intelligence in Bank Risk Analysis

This paper provides an overview of big data technologies and best practices from the standpoint of business intelligence (BI) applications in the banking industry. The authors discussed current challenges in banking industry that could be addressed by using big data technologies. Based on their research, they provided a list of big data tools and technologies in terms of an ecosystem that are suitable for real-time data processing and capable in bank fraud detection and prevention, and other bank risk analysis. They highlighted how business intelligence could be leveraged with the help of emerging big data technologies.