Fraud Detection Model Research Articles

AI-based model for fraud detection in bank systems

Due to the very high direct or indirect costs of fraud, banks and financial institutions seek to accelerate the recognition of the activities of fraudsters. The reason for this is its direct effect on serving the customers of these institutions, reducing operating costs and remaining as a reliable and valid financial service provider. On the other hand, in recent years, with the development of information and communication technology, electronic banking has become very popular. In the meantime, it is inevitable to use fraud detection techniques to prevent fraudulent actions in banking systems, especially electronic banking systems. In this paper, a method has been developed that leads to the improvement of fraud detection in information security and cyber defense systems. The main purpose of fraud detection systems is to predict and detect false financial transactions and improve the intrusion detection system using information classification. In this regard, the genetic algorithm, which is known as one of the stochastic optimization methods, is used. At the end, the results of the genetic algorithm have been compared with the results of the decision tree classification and the regression tree. The simulation results show the effectiveness and superiority of the proposed method.

Machine Learning Techniques for Fraud Detection in Financial Services

Fraudulent activities remain a critical challenge for financial institutions, prompting a continual quest for robust detection mechanisms. While effective to a certain extent, traditional rule-based approaches often fall short of capturing fraudsters' evolving tactics. In response, the integration of machine learning (ML) techniques has emerged as a promising avenue to bolster fraud detection capabilities. This paper presents a comprehensive review of recent advancements in ML algorithms specifically tailored for fraud detection within the realm of financial services. Leveraging a plethora of methodologies, including supervised, unsupervised, and semi-supervised learning, researchers and practitioners have endeavored to devise innovative strategies to combat fraudulent transactions effectively. Supervised learning techniques, such as logistic regression, decision trees, random forests, and gradient-boosting machines, have garnered significant attention due to their ability to learn from labeled data and discern patterns indicative of fraudulent behavior. Conversely, unsupervised learning methods, including clustering and anomaly detection algorithms, strive to identify transactional data irregularities without needing labeled examples. Semi-supervised learning techniques bridge the gap between supervised and unsupervised approaches by leveraging a small set of labeled data in conjunction with a larger pool of unlabeled data to enhance detection accuracy. A pivotal aspect in the efficacy of ML models for fraud detection lies in feature engineering. Given the unique characteristics of financial data, feature selection and transformation play a vital role in capturing meaningful signals while mitigating noise. Techniques such as PCA (Principal Component Analysis), LDA (Linear Discriminant Analysis), and feature scaling are commonly employed to preprocess data and extract relevant features that encapsulate the underlying patterns of fraudulent behavior. Despite the strides made in ML based fraud detection, several challenges persist. Imbalanced datasets, wherein fraudulent transactions constitute a minority class, pose a significant hurdle, potentially leading to biased models favoring the majority class. Addressing this imbalance requires careful consideration of sampling techniques, cost-sensitive learning, and performance metrics tailored to asymmetric distributions. Furthermore, the interpretability of ML models remains a pressing concern, particularly in highly regulated industries like finance. Explainable AI (XAI) methodologies, such as SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations), seek to elucidate the decision-making process of complex models, fostering trust and transparency in their deployment. Looking ahead, the convergence of emerging technologies, including deep learning and blockchain, holds promise for enhancing fraud detection capabilities. Deep learning architectures, with their ability to automatically extract hierarchical features from raw data, offer potential avenues for improving detection accuracy, albeit at the cost of interpretability. Similarly, the immutable nature of blockchain technology presents opportunities for enhancing data integrity and transaction transparency, thereby fortifying fraud prevention measures.

Credit Card Fraud Detection Model Based on Correlation Feature Selection

Credit card fraud is a widespread cybercrime that threatens financial security. Effective cybersecurity measures are essential to mitigate these risks. Machine learning has shown promising results in detecting credit card fraud by analyzing transaction data and identifying patterns of suspicious behavior. Feature selection is crucial in machine learning because it simplifies the model, improves its performance, and prevents overfitting. This research introduces a machine learning model designed for credit card fraud detection. The model makes use of three types of correlations. Pearson, Spearman, and Kendall, to identify features and enhance the fraud detection process. Testing on datasets yielded impressive results achieving category accuracies of 99.95% and 99.58% surpassing alternative approaches. Also, the results showed that Kendall correlation is the best among the three types of correlation in selecting attributes in all approved datasets.

Optimal Single-Valued Neutrosophic Sine Trigonometric Aggregation Operators for Accurate Financial Fraud Detection Model

Financial fraud may be regarded as any fraud targeting financial organisations including crypto exchanges, banks, fintech, and lending organizations, or any criminal activity associated with the payment process. Financial fraud detection cites protocol set prepared to circumvent the destruction produced by fraudulent activities happening in financial service suppliers. Ecological financial fraud detection (FD) includes the usage of ethical and sustainable performs within fraud actions recognition from the financial area. In recent times, DL and ML techniques have been used in CCF recognition owing to their ability to construct a robust mechanism to discover fraud businesses. Therefore, this study develops an Optimal Single Valued Neutrosophic Sine Trigonometric Aggregation Operator (O-SVNSTAO) for Accurate Financial Fraud Detection Model. The genetic-inspired particle swarm optimization (GIPSO) feature selection model efficiently discerns the relevant attribute from sophisticated financial databases, improving the model's discriminative power while alleviating dimensionality problems. Consequently, the SVNSTAO classifier leverages the features selected to discern complicated features inherent in fraudulent actions, which facilitates accurate diagnosis. Moreover, the COA parameter tuning mechanism enhances the SVNSTAO model's parameter, which ensures adaptability and optimum performance to varied fraud settings. Empirical analysis of real-time financial datasets demonstrates the superiority of O-SVNSTAO technique over classical methods, underlining its effectiveness in discovering financial fraud with exceptional efficiency and reliability

An Adaptive Heterogeneous Credit Card Fraud Detection Model Based on Deep Reinforcement Training Subset Selection

An Adaptive Heterogeneous Credit Card Fraud Detection Model Based on Deep Reinforcement Training Subset Selection

An analysis of Satyam case using bankruptcy and fraud detection models

Bankruptcy, which occurs due to inability of a business, to repay its debts and obligations has caught the interest of investors and practitioners alike. Predicting bankruptcy prior to the occurrence of event has become crucial in the field of investment and lending, especially in the light of events such as the global financial crisis of 2008. Early bankruptcy prediction models used traditional statistical techniques via financial ratios. Since then there has been a constant endeavour to develop models with enhanced predictive performance. Satyam Inc. was Indian listed business which went bankrupt in 2007. In this study we apply financial models such as F score, M-score and Z-score to show how common/retail investor who cannot use sophisticated financial tool, can benefit from these simple tools and make good investment decisions. Our research adds to the discussion regarding the capability of bankruptcy prediction models. We derive our findings using the data for Satyam Inc., one of the biggest corporate scandalin India. Before the scam, Beinish M-score acted as more efficient predictor of bankruptcy and fraud than Altman Z-score and Peotroski F score. In fact, the usefulness of Z score and F score was average to poor in predicting Satyam’s bankruptcy in advance. This result contradicts outcomes from several researches who had found a great ultility of Z score and F score. From the policy view, the regulator of financial market can protect the financial illiterate investor who makes investment in capital market to take informed investment decision by using the Beinish M-score for making investing decision in the stock of the company. Similarly, these models can be used by banks and financial institutions in case of existing as well as potential corporate borrowers.

Review Paper on UPI Fraud Detection Using Machine Learning

Abstract: With the rapid growth of digital transactions, the Unified Payments Interface (UPI) has emerged as a popular and convenient method for financial transactions in the modern era. However, the increasing reliance on digital platforms has also led to a rise in fraudulent activities. This paper proposes a robust UPI fraud detection system employing advanced machine learning techniques to enhance the security of digital transactions. The proposed system leverages a diverse set of features, including transactional patterns, user behaviour, and device information, to create a comprehensive model for fraud detection. Machine learning algorithms, such as supervised learning classifiers and anomaly detection techniques, are employed to analyse historical transaction data and identify patterns indicative of fraudulent activities. The model is trained on a labelled dataset that includes both genuine and fraudulent transactions, ensuring its ability to distinguish between normal and suspicious behaviour

Credit Card Fraud Detection Using Machine Learning Algorithms

Abstract: The study delves into the prevalent issue of credit card fraud within electronic transactions. Employing machine learning techniques, including logistic regression, the research focuses on the development and evaluation of fraud detection models. The dataset undergoes comprehensive pre-processing steps, addressing common issues such as missing data and class imbalance. Various machine learning algorithms are explored, with a specific emphasis on logistic regression. Sampling techniques are employed to balance the dataset, ensuring equal representation of legitimate and fraudulent transactions. Model evaluation involves metrics like accuracy, precision, recall, and F1 score to assess performance. The outcomes shed light on the logistic regression model's effectiveness in detecting fraudulent transactions. The study also outlines inherent limitations, acknowledging challenges such as imbalanced datasets and the dynamic nature of fraud tactics. In summary, this research contributes to the ongoing advancements in credit card fraud detection, utilizing machine learning for enhanced security in electronic transactions. The discussed findings and limitations serve as a valuable foundation for future developments in this critical field.

Credit Card Fraud Detection and Identification using Machine Learning Techniques

Fraudulent internet transactions have caused considerable harm and losses for both people and organizations over time. The growth of cutting-edge technology and worldwide connectivity has exacerbated the rise in online fraud instances. To offset these losses, robust fraud detection systems must be developed. ML and statistical approaches are critical components in properly recognizing fraudulent transactions. However, implementing fraud detection models presents challenges such as limited data availability, data sensitivity, and imbalanced class distributions. The confidentiality of records adds complexity to drawing inferences and constructing improved models in this domain. This research explores multiple algorithms suitable for classifying transactions as either genuine or fraudulent using the Credit Card Fraud dataset. Given the extremely unbalanced nature of the dataset, the SMOTE approach was used for oversampling to alleviate the class distribution imbalance. In addition, feature selection was carried out, and the dataset was divided into training and test data. The experiments utilized NB, RF, and MLP algorithms, all of which demonstrated high accuracy in detecting credit card fraud. MLP method achieved 99.95% accuracy as compared to other methods.

An Ensemble-based Fraud Detection Model for Financial Transaction Cyber Threat Classification and Countermeasures

Fraud remains a pervasive challenge within the banking industry, where financial institutions and their clients grapple with substantial annual losses. The proliferation of digital transactions and online banking has created new avenues for fraudsters to exploit vulnerabilities, leading to financial harm to unsuspecting victims. Consequently, the imperative to promptly and accurately detect fraudulent transactions has grown significantly, both as a safeguard against financial crimes and as a pillar of trust between customers and the banking sector. This paper introduces an innovative fraud detection model designed for bank payment transactions using advanced ensembling techniques. This study presents a comprehensive evaluation of an ensembling model conducted on the Bank Account Fraud (BAF) dataset. Through meticulous analysis, the performance of various base models and ensembling methods was assessed and compared, employing a variety of critical metrics including accuracy, precision, recall, and F1-score. The proposed ensemble model, referred to as "Stacking," exhibited remarkable performance, attaining a commendable accuracy score of 0.98. This result reaffirmed its prowess as a comprehensive and balanced solution to the multifaceted challenges of fraud detection. This study has paramount implications for the banking industry, offering a robust and adaptable solution to deal with the increasing threats posed by financial fraud. Furthermore, it emphasizes the significance of precision-recall trade-offs in fraud detection and underscores the potential of ensemble methods, particularly the "Stacking" model, to fortify the resilience and efficacy of existing security systems.

Research on Financial Fraud Detection Models Integrating Multiple Relational Graphs

The current fraud risk in digital finance is increasing year by year, and the mainstream solutions rely on the inherent characteristics of users, which makes it difficult to explain fraud behaviors and fraud behavior patterns are less researched. To address these problems, we propose an integrated multiple relational graphs fraud detection model Tri-RGCN-XGBoost, which analyzes the impact of user association patterns on fraud detection by mining the behavioral associations of users. The model builds a heterogeneous information network based on real transaction data, abstracts three types of bipartite graphs (user–device, user–merchant, and user–address), aggregates the information of the user’s neighbor nodes under the three types of behavioral patterns, and integrates the graph convolution classification results under the three behavioral patterns with the XGBoost model to achieve fraudulent user detection with integrated multiple relational graphs. The results show that the performance of this model in fraud identification is significantly improved, especially in reducing the fraudulent user underreporting rate. Further, the behavioral associations that play a key role in fraud user identification are analyzed in conjunction with shape value to provide a reference for fraud pattern mining.

Financial transaction fraud detector based on imbalance learning and graph neural network

Fraud detection is a vital and challenging task in the financial domain. Traditional machine learning approaches have limitations in dealing with the high imbalance and complexity of transaction data and the stealthy behavior of fraudulent entities. To address these issues, we propose HHLN-GNN, a graph neural network-based fraud detection model that leverages subgraph generators and neighborhood samplers to handle category imbalance. Furthermore, the model employs a self-attentive module and differentiates between homogeneous and heterogeneous connections to reduce the artificiality of fraudulent nodes and exploit the hidden information of transaction data more fully. We conduct experiments on three real-world public benchmark datasets, YelpChi, Amazon and Elliptic, and demonstrate that our model significantly outperforms existing benchmark methods on various performance metrics, such as F1-macro, AUC and GMean, by 10%, 12.5%, and 17.3% on YelpChi, 0.7%, 2.5% and 4% on Amazon, respectively.

Enhancement of Backward Feature Elimination as a Pre-Processing Method for K Nearest Neighbor Algorithm Applied to Insurance Fraud Detection

This study presents novel enhancements to the Backward Feature Elimination (BFE) method for improved insurance fraud detection using the K-Nearest Neighbor (KNN) algorithm. The research addresses issues inherent in the baseline BFE process, such as the over-reliance on p-values, the potential for misleading results, and suboptimal feature selection leading to overfitting. To address these, the study integrates confidence intervals and feature importance into the BFE process, establishing a more robust and reliable criterion for feature selection. Moreover, feature engineering techniques are introduced during preprocessing to enhance model performance. The modified BFE method demonstrates superior performance over the baseline model regarding the recall, precision, and F1 score. Stratified K-Fold Cross-Validation, ROC-AUC Score, and Coefficient of Variation (CV) confirm the consistency and robustness of the enhanced model across varying data subsets. These innovations offer a comprehensive and reliable solution to feature selection in the BFE method, applied to the KNN model for effective insurance fraud detection. The study mitigates the issues related to p-value dependence and boosts model performance, paving the way for more accurate and robust fraud detection systems.

Explainable machine learning models for Medicare fraud detection

As a means of building explainable machine learning models for Big Data, we apply a novel ensemble supervised feature selection technique. The technique is applied to publicly available insurance claims data from the United States public health insurance program, Medicare. We approach Medicare insurance fraud detection as a supervised machine learning task of anomaly detection through the classification of highly imbalanced Big Data. Our objectives for feature selection are to increase efficiency in model training, and to develop more explainable machine learning models for fraud detection. Using two Big Data datasets derived from two different sources of insurance claims data, we demonstrate how our feature selection technique reduces the dimensionality of the datasets by approximately 87.5% without compromising performance. Moreover, the reduction in dimensionality results in machine learning models that are easier to explain, and less prone to overfitting. Therefore, our primary contribution of the exposition of our novel feature selection technique leads to a further contribution to the application domain of automated Medicare insurance fraud detection. We utilize our feature selection technique to provide an explanation of our fraud detection models in terms of the definitions of the selected features. The ensemble supervised feature selection technique we present is flexible in that any collection of machine learning algorithms that maintain a list of feature importance values may be used. Therefore, researchers may easily employ variations of the technique we present.

A distributed deep neural network model for credit card fraud detection

This paper develops a distributed neural network model (DDNN) for detecting credit card fraud to federate credit card transaction data among different financial institutions. In addition, the convergence of the DDNN model is achieved by introducing a model optimization algorithm. The results demonstrate that (1) The use of a distributed model can avoid privacy leakage and data handling costs; (2) The DDNN model accelerates the convergence of the model through simultaneous computation of multiple clients; (3) The DDNN model detects credit card fraud better than multiple types of centralized models.

Fraud Detection and Identification in Credit Card Based on Machine Learning Techniques

Fraudulent internet transactions have caused considerable harm and losses for both people and organizations over time. The growth of cutting-edge technology and worldwide connectivity has exacerbated the rise in online fraud instances. To offset these losses, robust fraud detection systems must be developed. ML and statistical approaches are critical components in properly recognizing fraudulent transactions. However, implementing fraud detection models presents challenges such as limited data availability, data sensitivity, and imbalanced class distributions. The confidentiality of records adds complexity to drawing inferences and constructing improved models in this domain. This research explores multiple algorithms suitable for classifying transactions as either genuine or fraudulent using the Credit Card Fraud dataset. Given the extremely unbalanced nature of the dataset, the SMOTE approach was used for oversampling to alleviate the class distribution imbalance. In addition, feature selection was carried out, and the dataset was divided into training and test data. The experiments utilized NB, RF, and MLP algorithms, all of which demonstrated high accuracy in detecting credit card fraud. MLP method achieved 99.95% accuracy as compared to other methods

Graph transformer-based self-adaptive malicious relation filtering for fraudulent comments detection in social network

In recent years, Graph Neural Networks (GNNs) have proven to be effective in detecting fraud within social networks by gathering information from neighboring nodes to predict fraudulent actions. However, the continuous evolution and camouflaging tactics of fraudsters, such as establishing malicious connections with legitimate users and mimicking their behaviors, can often elude GNN-based detection methods. To tackle this issue, we propose a multi-order moments graph transformer, named MMGT, to effectively learn node representations through an attention mechanism that captures information of different orders of moments. Building upon this foundation, we further introduce a self-adaptive malicious relation filtering model for fraud detection, denoted as SFGT. Initially, we form a multi-relational graph that encapsulates complex relations within a given social network and derive node representations by accumulating neighbor node and edge data based on an enhanced graph transformer. Subsequently, a threshold-based malicious relation filter mechanism is proposed to eliminate malicious links by assessing the distance between nodes. Furthermore, an adaptive threshold learning policy is developed to bolster the model’s performance and its ability to generalize. Finally, extensive experiments are carried out on two public datasets, Amazon and YelpChi, which underscores the effectiveness of our proposed model. The experimental results indicate that our model achieves state-of-the-art performance.

Assessing the Effectiveness of Fraud Detection Models

Our research seeks to determine whether the detection of potential fraud in China-listed U.S. companies could be measured by the U.S. M-Score and F-Score models. Then, we group the data for 18 pairs of companies to make a t-test defined as the average difference between firms' M-scores or F-scores before and after the fraud. Moreover, more detailed information about these companies, financial fraud methods and motives, and ways to prevent financial fraud. Through our preliminary research, these two U.S. detection models can be used to detect financial fraud for Chinese-listed U.S. companies in statistical significance. With this premise, we also do an additional t-test to prove the standing adverse effects of committing fraud on firms.

Feature-wise attention based boosting ensemble method for fraud detection

Transaction fraud detection is an essential topic in financial research, protecting customers and financial institutions from suffering significant financial losses. The existing ensemble learning-based fraud detection model improves the accuracy of distinguishing fraudulent transactions by integrating multiple base learners. However, their insufficient optimization of variance leads to the inadequate generalization of the model, which makes them unable to meet the requirements of detecting evolving fraudulent behavior. So, this paper proposes an attention-based boosting ensemble model (AM-Boost) for fraud detection that imparts diversity perturbation to the base learner from the feature dimension. Firstly, the feature-wise attention mechanism is proposed to help the model exploit the highly informative fraud features by learning distributed global feature space. And it is combined with boosting strategy to reduce the variance by lowering the correlation between base learners. Secondly, we propose a feature diversity regularization term in the integration stage to optimize the weighted majority voting strategy with a reasonable trade-off between bias and variance. Finally, we evaluate the proposed method on a public fraud detection dataset and a real-world transaction dataset. Extensive experiment results demonstrate that our method outperforms the baseline methods in both datasets.

A Survey and a Credit Card Fraud Detection and Prevention Model using the Decision Tree Algorithm

Today, many people prefer online payment methods due to the rapid growth in cashless electronic transactions. Credit and debit cards are the most popular electronic payment methods but are prone to fraud due to the nature of their use and the tendency of fraudsters to access their details. This study proposes a theoretical credit fraud detection and prevention model using a Decision Tree Algorithm (DCA). Moreover, a survey questionnaire was used to investigate students' perceptions of credit card fraud incidents. Data were collected from 102 students from different universities and countries around the world. The results showed that 95.9% of the respondents knew how credit/debit card fraud occurs, while 4.1% of them did not. Finally, 81.6% expressed their willingness to use a tool based on the proposed model to prevent or detect credit/debit card fraud incidents.