Detection Of Fraudulent Transactions Research Articles

Enhancing fraud detection in banking by integration of graph databases with machine learning

The banking sector's shift from traditional physical locations to digital channels has offered customers unprecedented convenience and increased the risk of fraud for customers and institutions alike. In this study, we discuss the pressing need for robust fraud detection & prevention systems in the context of evolving technological environments. We introduce a graph-based machine learning model that is specifically designed to detect fraudulent activity in various types of banking operations, such as credit card transactions, debit card transactions, and online banking transactions. This model uses advanced methods for anomalies, behaviors, and patterns to analyze past transactions and user behavior almost immediately. We provide an in-depth methodology for evaluating fraud detection systems based on parameters such as Accuracy Recall rate and False positive rate ROC curves. The findings can be used by financial institutions to develop and enhance fraud detection strategies as they demonstrate the effectiveness and reliability of the proposed approach. This study emphasizes the critical role that innovative technologies play in safeguarding the financial sector from the ever-changing strategies of fraudsters while also enhancing banking security.•This paper aims to implement the detection of fraudulent transactions using a state-of-the-art Graph Database approach.•The relational graph of features in the dataset used is modelled using Neo4J as a graph database.•Applying JSON features from the exported graph to various Machine Learning models, giving effective outcomes.

Leveraging Adversarial Augmentation on Imbalance Data for Online Trading Fraud Detection

Nowadays, the emergence of online trading greatly facilitates people’s life. Meanwhile, online trading also brings hidden dangers, such as online fraudulent trading. To solve the issue, researchers have proposed many different detection models. However, in actual business scenarios, fraudulent transactions usually only account for a small portion of normal transactions, resulting in extremely imbalanced data. Besides, the concealment of fraud is reflected in that the fraudsters are imitating the normal transactions of users, posing a huge challenge for fraudulent transaction detection modeling. Inspired by generative adversarial networks (GANs), we propose a GAN-based framework to detect online banking fraud on extremely imbalanced data, called BalanceGAN. A fraud detection model is first pretrained using the data generated by the generator and then the model is fine-tuned using transfer learning on real-world datasets, by using this approach to address data imbalances. Compared with the conventional methods for solving imbalanced data, our BalanceGAN can avoid over-fitting of the model relatively, experiments on two real datasets show that our BalanceGAN has more than 10% performance improvement in Precision and Recall.

Machine Learning in Financial Transaction Fraud Detection and Prevention

In this paper, we delve into the application of machine learning technology in financial transaction fraud detection and prevention, including how it works, real-world examples, the challenges it faces, and its potential impact on the future of the financial security space. As the digitalisation process of the financial industry accelerates, the means of financial transaction fraud are becoming increasingly complex and varied, bringing great risks to individuals, enterprises and even the entire financial system. In this context, traditional fraud detection methods are increasingly difficult to effectively respond to emerging frauds due to their inherent limitations. In contrast, machine learning, with its powerful data processing capability, complex pattern recognition ability, and self-learning and adaptation, is considered a powerful tool against financial transaction fraud. Through real-world case studies, we demonstrate how the application of machine learning techniques in financial fraud detection and prevention has helped financial institutions improve detection efficiency and accuracy. Covering a wide range of financial fraud types from credit card fraud to account hijacking to money laundering, these cases illustrate how machine learning models can play a role in monitoring transaction activities in real time, identifying unusual behaviours, and adapting to new fraudulent tactics. In addition, we also discuss the main challenges faced when implementing machine learning techniques, including data quality and privacy protection, model interpretability, the cost of implementing the technology, and integration issues with existing systems. While machine learning shows great potential for financial transaction fraud detection and prevention, there are a number of technical and practical challenges that need to be overcome in order to take full advantage of its benefits. These include improving the efficiency of data collection and processing, ensuring transparency and interpretability of models, reducing the cost of technology implementation and enhancing cross-industry collaboration. In the future, as machine learning and related technologies continue to advance, and as financial institutions and regulators gain a deeper understanding of these technologies, it is reasonable to believe that machine learning will play an even more important role in the financial security field, contributing to the building of a safer, more efficient and smarter financial system. The goal of this paper is to provide financial institutions, technology developers, and policy makers with a comprehensive view of the application of machine learning in financial transaction fraud detection and prevention, pointing out future directions and potential strategies by analysing its advantages, challenges, and practical examples. Through this research, we hope to facilitate broader industry discussions, promote innovation and development of financial security technologies, and jointly address the challenges of financial fraud.

Credit card transaction fraud detection based on machine learning

The rise of e-commerce payment systems has been swift due to our society's rapid advancement. However, the transparency and vulnerability of the internet have opened new avenues for illicit access, resulting in a significant surge in financial fraud cases, particularly credit card transaction fraud. Detecting and mitigating such incidents has become crucial. One branch of artificial intelligence, machine learning, offers a potent solution. It operates by utilizing various algorithms and models that rely on established patterns and reasoning without explicit instructions. By processing vast amounts of historical data, machine learning models can identify underlying data relationships, allowing them to make accurate predictions based on input data. Therefore, it emerges as a highly effective method for credit card transaction fraud detection. This paper reviews the research methods for credit card fraud, introduces credit card transaction fraud detection data sets, and outlines machine learning algorithms and models related to credit card fraud detection. It also considers the future prospects for machine learning development and possible challenges.

Transaction fraud detection by CatBoost model with feature engineering

In the digital era, the ubiquitous nature of online transactions has placed a spotlight on the imperative for robust fraud detection systems. This research tackles this pressing issue, with a particular emphasis on the nuanced process of feature engineering tailored for the IEEE-CIS dataset, a representative sample of contemporary transactional behaviors. The enhancement of data attributes, through meticulous feature engineering, acts as the bedrock for the application of the CatBoost model - a gradient-boosting technique revered for its precision. The paper dedicates significant attention to both these pivotal stages, showcasing the synergistic effect they have when applied in tandem. With this refined data and sophisticated modeling, the proposed method manifests exceptional performance, establishing a new benchmark when juxtaposed with other gradient-boosting methodologies. Conclusively, this study offers valuable insights for enhancing online transaction security and sets the stage for further innovation in fraud detection within the machine learning community.

Advanced Fraud Detection in Blockchain Transactions: An Ensemble Learning and Explainable AI Approach

In recent years, cryptocurrencies have experienced rapid growth and adoption, revolutionizing the financial sector. However, the rise of digital currencies has also led to an increase in fraudulent transactions and illegal activities. In this paper, we present a comprehensive study on the detection of fraudulent transactions in the context of cryptocurrency exchanges, with a primary focus on the Ethereum network. By employing various Machine Learning (ML) techniques and ensemble methods, including the hard voting ensemble model, which achieved a remarkable 99% accuracy, we aim to effectively identify suspicious transactions while maintaining high accuracy and precision. Additionally, we delve into the importance of eXplainable Artificial Intelligence (XAI) to enhance transparency, trust, and accountability in AI-based fraud detection systems. Our research contributes to the development of reliable and interpretable models that can significantly improve the cryptocurrency ecosystem security and integrity.

Dandelion optimization based feature selection with machine learning for digital transaction fraud detection

<abstract> <p>Digital transactions relying on credit cards are gradually improving in recent days due to their convenience<bold>.</bold> Due to the tremendous growth of e-services (e.g., mobile payments, e-commerce, and e-finance) and the promotion of credit cards, fraudulent transaction counts are rapidly increasing. Machine learning (ML) is crucial in investigating customer data for detecting and preventing fraud. Conversely, the advent of irrelevant and redundant features in most real-time credit card details reduces the execution of ML techniques. The feature selection (FS) approach's purpose is to detect the most prominent attributes required for developing an effective ML approach, making sure that the classification and computational complexity are improved and decreased, respectively. Therefore, this study presents an evolutionary computing with fuzzy autoencoder based data analytics for credit card fraud detection (ECFAE-CCFD) technique. The purpose of the ECFAE-CCFD technique is to recognize the presence of credit card fraud (CCF) in real time. To achieve this, the ECFAE-CCFD technique performs data normalization in the earlier stage. For selecting features, the ECFAE-CCFD technique applies the dandelion optimization-based feature selection (DO-FS) technique. Moreover, the fuzzy autoencoder (FAE) approach can be exploited for the recognition and classification of CCF. FAE is a category of artificial neural network (ANN) designed for unsupervised learning that leverages fuzzy logic (FL) principles to enhance the representation and reconstruction of input data. An improved billiard optimization algorithm (IBOA) could be implemented for the optimum selection of the parameters based on the FAE algorithm to improve the classification performance. The simulation outcomes of the ECFAE-CCFD algorithm are examined on the benchmark open-access database. The values display the excellent performance of the ECFAE-CCFD method with respect to various measures.</p> </abstract>

Enhancing Credit Card Fraud Detection on Imbalanced Datasets

Abstract. The use of credit cards is becoming more and more popular in today's society, especially now with the prevalence of electronic payments. Electronic payments are available through bank apps or payment processors like PayPal, Alipay, etc. Without using cash, people are taking advantage of the convenience of them both online and offline. In addition to bringing people the benefits of efficiency and convenience, credit card fraud has also emerged and caused a great deal of economic losses for cardholders, as well as causing great trouble for banks. The primary goal of this work is to identify fraudulent transactions in an unbalanced dataset. The dataset comprises credit card transactions from just two days in 2013 in Europe. In this study, the original data, the original data with Stratified-KFolds, and the undersampled data will be compared. It is found that undersampling, although it reduces the accuracy by a small amount, can greatly improve the detection of fraudulent transactions. Meanwhile, this study uses different models, one is Logistic Regression, and the others are all Tree-Based method. The study analyzes their confusion matrices, ROC curves, and Precession-Recall curves. The results show that in the undersampling rate dataset, the recall, precision, F1 score and accuracy of Xgboost are optimized to 93.2%, 97%, 95%, and 95% respectively, and the AUC of both the ROC curve and the Presicion-Recall curve are optimized to 99%, so this study concludes that XGboost is the best performer. With excellent algorithms, we can better avoid the leakage of information and loss of money in real life.

Real-time Card Fraud Detection: A Stacked Ensemble Machine Learning Approach

As the world witnesses, Card fraud detection has become more and more crucial observes a rise in new and innovative techniques used by offenders. Machine learning and artificial intelligence are at the forefront of the various fraud detection technologies that credit card firms and financial institutions are utilizing. The Nilson Report's startling statistics demonstrate the scope of the issue. Global losses due to credit and debit card fraud increased to $40.6 billion in 2022, up from $34.7 billion the year before, with the United States being responsible for a large 36.8% of these losses. Additionally, younger people, especially those between the ages of 20 and 29, were disproportionately affected by credit card fraud. Our study is unique in a number of respects. We used a carefully controlled dataset to guarantee that the values after preprocessing were accurate and complete. We enhanced the detection of fraudulent transactions by our model by using a wide range of features. To find the most suitable machine learning algorithm for our dataset, we also investigated a range of other techniques. Our study used a dataset with the transaction histories of over 300,000 people from the UCI Machine Learning Repository. We determined the best methods through a multi-step procedure that included data pretreatment, dataset division, and model selection. Our study is unique in a number of respects. We used a carefully controlled dataset to guarantee that the values after preprocessing were accurate and complete. We enhanced the detection of fraudulent transactions by our model by using a wide range of features. To find the most suitable machine learning algorithm for our dataset, we also investigated a range of other techniques. The Stacked Ensemble model, SN Algorithm (SVM + Naive Bayes), beat other models according to the results in terms of important metrics. Impressive AUC, CA, F1, accuracy, and recall ratings demonstrated the system's effectiveness in preventing card theft.

Efficient Fraud Detection in Ethereum Blockchain through Machine Learning and Deep Learning Approaches

Background: This paper tackles the critical challenge of detecting fraudulent transactions within the Ethereum blockchain using machine learning techniques. With the burgeoning importance of blockchain, ensuring its security against fraudulent activities is crucial to prevent significant monetary losses. We utilized a public dataset comprising 9,841 Ethereum transactions, characterized by attributes such as gas price, transaction fee, and timestamp.Methods: Our approach is bifurcated into two core phases: data preprocessing and predictive modeling. In the data preprocessing phase, we meticulously process the dataset and extract pivotal features from transactions, setting the stage for efficient predictive modeling.Findings: For predictive modeling, we employed several machine learning algorithms to discern between fraudulent and legitimate transactions. Our evaluation encompassed algorithms like decision trees, logistic regression, gradient boosting, XGBoost, and an innovative hybrid model that melds random forests with deep neural networks (DNN).Novelty: Our findings underscore that the proposed model boasts a precision rate of 97.16%, marking a substantial leap in fraudulent transaction detection on the Ethereum blockchain in comparison to prevailing methodologies. This paper augments the current efforts aimed at bolstering the security of blockchain transactions using sophisticated analytical strategies..

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.

Detection of fraudulent transactions using artificial neural networks and decision tree methods

The accounting systems generate a large amount of data due to financial transactions. Intentionally fraudulent transactions can occur in high-dimensional and large numbers of emerging data. While many methods can be used for the estimation and detection of fraudulent transactions in accounting, which differ in the audit process, scope and application method, data mining methods can also be used today due to a large number of data and the desire not to narrow the scope of the audit. This study tested the accuracy of detecting fraudulent transactions using artificial neural networks and decision tree methods. According to the results of the analysis test data set for detecting fraud or error risk, 99.7981% accuracy was obtained in the artificial neural networks method and 99.9899% in the decision tree method.

Application of Artificial Intelligence for Fraudulent Banking Operations Recognition

This study considers the task of applying artificial intelligence to recognize bank fraud. In recent years, due to the COVID-19 pandemic, bank fraud has become even more common due to the massive transition of many operations to online platforms and the creation of many charitable funds that criminals can use to deceive users. The present work focuses on machine learning algorithms as a tool well suited for analyzing and recognizing online banking transactions. The study’s scientific novelty is the development of machine learning models for identifying fraudulent banking transactions and techniques for preprocessing bank data for further comparison and selection of the best results. This paper also details various methods for improving detection accuracy, i.e., handling highly imbalanced datasets, feature transformation, and feature engineering. The proposed model, which is based on an artificial neural network, effectively improves the accuracy of fraudulent transaction detection. The results of the different algorithms are visualized, and the logistic regression algorithm performs the best, with an output AUC value of approximately 0.946. The stacked generalization shows a better AUC of 0.954. The recognition of banking fraud using artificial intelligence algorithms is a topical issue in our digital society.

Credit Card Fraudulent Transaction Detection and Prevention

Abstract: In recent years, online payment methods have been used widely as an outcome of the rapid increase in non-cash and digital electronic transactions. Credit cards represent one of the electronic payment methods. With the advancement of online payments in various products and services, the likelihood of credit card fraud has risen compared to the decades-long history of credit cards. The credit card frauds can be detected by evaluating the credit card purchasing patterns using the historical data in order to detect the frauds. This data evaluation can help the banks or other organizations offering credit cards to minimize their losses due to the credit card frauds. The historical data evaluation with the current purchasing patterns requires statistical modeling, which can automatically evaluate the fraudulent patterns and alarm the banks about the transactions. This helps the banks for early detection of the frauds, where they can easily eliminate the credit card frauds by declining the suspected transactions. And also blockchain technology is applied to prevent the hacker to view customers details so that fraudsters can't use stolen credit card information to open new accounts, obtain loans, and engage in other illegal activities. Credit card fraud detection and prevention have become essential for banks and other financial institutions to safeguard their customers' financial transactions. This paper presents an overview of credit card fraud detection and prevention techniques.

The Planning Process of the Online Transaction Fraud Detection Using Backlogging on an E-Commerce Website

E-commerce is defined as the selling and purchasing of products, as well as the transmission of data or payments, through an electronic network. E-commerce is driven by the internet, where customers can browse through an online store and place orders for items or services using their own devices. Online transactions are used by e-commerce businesses to charge customers for goods and services. The rising number of online transactions has increased the number of payment frauds. Payment fraud refers to any type of fraudulent or illegal transaction carried out by a cybercriminal. The criminal utilizes the internet to deprive the victim of money, personal property, or sensitive information. The objective of this research is to investigate the gaps in the existing online transaction fraud detection on e-commerce websites, to propose and develop an online transaction fraud detection using backlogging on e-commerce websites that is safe against fraud and enables simple and efficient transactions and implement security measures to prevent a breach of the proposed system. The method for this research is using the Waterfall methodology as a Software Development Life Cycle.

ASA-GNN: Adaptive Sampling and Aggregation-Based Graph Neural Network for Transaction Fraud Detection

ASA-GNN: Adaptive Sampling and Aggregation-Based Graph Neural Network for Transaction Fraud Detection

An efficient credit card fraud detection approach using cost‐sensitive weak learner with imbalanced dataset

AbstractThe usage of credit cards is increasing in the digitization era to purchase goods online or offline, and the number of fraudulent credit card transactions has also increased. Fraudulent transaction detection is a big issue in the credit card domain because the data set is highly imbalanced. The performance of traditional classification techniques has decreased due to this issue. Therefore, a dynamic fraud detection system is required to overcome this issue. This article proposes an efficient cost‐sensitive weak learner approach with a bagging and random forest classifier (CSWLB) to minimize misclassification problems and overcome the class imbalance issue. The proposed CSWLB approach is included in an adaptive algorithmic method (cost‐sensitive) with two weak learner ensembles (bagging‐random forest), which assigns high weight to the fraudulent transactions using the cost‐sensitive learning classifier and generates weight bags using a bagging ensemble classifier. Therefore, the random forest classifier has been implemented on the weight bags to improve classification accuracy. The proposed approach's effectiveness and efficiency have been computed on the Brazilian credit card data set. Its results have been compared with sampling, cost‐sensitive, and machine learning techniques. The proposed CSWLB approach obtained 765 total costs and 97.361% accuracy. The experimental results show that the proposed approach has outperformed compared to other techniques.

A multi-level weighted concept drift detection method

The concept drift detection method is an online learner. Its main task is to determine the position of drifts in the data stream, so as to reset the classifier after detecting the drift to improve the learning performance, which is very important in practical applications such as user interest prediction or financial transaction fraud detection. Aiming at the inability of existing drift detection methods to balance the detection delay, false positives, false negatives, and space–time efficiency, a new level transition threshold parameter is proposed, and a multi-level weighted mechanism including "Stable Level-Warning Level-Drift Level" is innovatively introduced in the concept drift detection. The instances in the window are weighted in levels, and the double sliding window is also applied. Based on this, a multi-level weighted drift detection method (MWDDM) is proposed. In particular, two variants which are MWDDM_H and MWDDM_M are proposed based on Hoeffding inequality and Mcdiarmid inequality, respectively. Experiments on artificial datasets show that MWDDM_H and MWDDM_M can detect abrupt and gradual concept drift faster than other comparison algorithms while maintaining a low false positive ratio and false negative ratio. Experiments on real-world datasets show that MWDDM has the highest classification accuracy in most cases while maintaining good space time efficiency.

Sequential Pattern Mining Approach for Personalized Fraudulent Transaction Detection in Online Banking

Financial institutions face challenges of fraud due to an increased number of online transactions and sophisticated fraud techniques. Although fraud detection systems have been implemented to detect fraudulent transactions in online banking, many systems just use conventional rule-based approaches. Rule-based detection systems have a difficulty in updating and managing their rules and conditions manually. Additionally, generated from the few fraud cases, the rules are general rather than specific to each user. In this paper, we propose a personalized alarm model to detect frauds in online banking transactions using sequence pattern mining on each user’s normal transaction log. We assumed that a personalized fraud detection model is more effective in responding to the rapid increase in online banking users and diversified fraud patterns. Moreover, we focused on the fact that fraudulent transactions are very different from each user’s usual transactions. Our proposed model divides each user’s log into transactions, extracts a set of sequence patterns, and uses it to determine whether a new incoming transaction is fraudulent. The incoming transaction is divided into multiple windows, and if the normal patterns are not found in the consecutive windows, an alarm is sounded. We applied the model to a real-world dataset and showed that our model outperforms the rule-based model and the Markov chain model. Although more experiments on additional datasets are needed, our personalized alarm model can be applied to real-world systems.

A novel framework for online transaction fraud detection system based on deep neural network

Since the introduction of online payment systems, people have started doing online transactions which has also led to the rise of fraudulent transactions causing loss of money to the users and created distrust in the usage of online payment systems. Hence, fraud detection systems are the need of the hour. Among the transactions occurring on daily basis, frauds are less in number as compared to the genuine transactions, so class imbalance naturally exists in fraud detection systems. In this research work, a novel framework for online transaction fraud detection system based on Deep Neural Network (DNN) has been proposed by utilizing algorithm-level method capable to detect frauds from imbalanced data and to maintain the overall performance of the model as well. The proposed system optimizes the decision threshold by utilizing the validation data efficiently for deciding whether an incoming transaction is a Fraud or not. For demonstration of the efficiency of our proposed system, three class imbalanced publicly available datasets have been used. Proposed system has shown better performance than data-level method. The results produced by the proposed fraud detection system have also been compared with existing machine learning techniques-based fraud detection systems. The experimental results show that the deep learning-based fraud detection system is more efficient for detecting frauds from imbalanced datasets having large number of input features as compared to the machine learning models.