Fraudulent Transactions Research Articles

Credit Card Fraud Detection: A Machine Learning Approach

The proliferation of credit card transactions in the digital era has exacerbated the threat of fraudulent activities, necessitating robust detection mechanisms. Traditional rule-based systems often fail to keep pace with the evolving nature of fraud, prompting the adoption of machine learning techniques. This research paper explores the efficacy of machine learning algorithms in detecting credit card fraud, aiming to enhance the security of financial transactions. Leveraging a comprehensive dataset comprising legitimate and fraudulent transactions, various machine learning models are trained, tested, and evaluated. Through meticulous experimentation and analysis, key insights are unearthed regarding the performance, strengths, and limitations of different algorithms in detecting fraudulent patterns. Furthermore, feature engineering techniques and ensemble learning methods are employed to improve detection accuracy and efficiency. The findings underscore the significance of adopting a holistic machine learning approach for credit card fraud detection, offering valuable insights for financial institutions and stakeholders in fortifying transaction security. Keywords: Credit card transactions, digital era, fraudulent activities, detection mechanisms, rule-based systems, credit card fraud.

CredChecker:-Credit Card Fraud Detection WebApp

The proposed app will serve as a crucial tool for detecting and managing credit card fraud, focusing on high-risk areas such as densely populated urban centers. The primary objective is to minimize financial losses incurred due to fraudulent activities. A key advantage lies in its seamless integration with financial institutions and law enforcement agencies, facilitating efficient collaboration between users and relevant authorities in case of suspected fraud incidents. This integration is pivotal in achieving the app's fundamental mission of delivering real-time fraud alerts, including transaction details, potential fraudulent activities, and recommended actions. The application will feature advanced functionalities tailored for individual users to enhance their awareness and preparedness against fraudulent transactions. These tools empower users to set up personalized fraud detection plans, allowing them to proactively monitor their accounts and swiftly respond to suspicious activities. To ensure the reliability and promptness of information, the app will leverage a combination of cuttingedge technologies, including machine learning algorithms and real-time data processing.

Credit Card Fraud Detection System

Globally, credit card fraud is a serious threat to people, businesses, and financial institutions. With the rise of online transactions, fraudsters have developed clever ways to take advantage of loopholes in payment systems. Traditional fraud detection methods based on manual inspections and rules-based systems are unable to counteract this new and evolving risk. As a result, the use of data analytics and machine learning has become a viable option for real-time detection and prevention of credit card fraud. The paper looks at using machine learning algorithms such as logistic regression, decision trees, random forests, neural networks, etc. to detect fraudulent transactions We go over the importance of data sources and components, analytical metrics, and how fraud detection on the effectiveness of examples. In addition, we list the current challenges and directions in which credit card fraud detection is likely to continue, including the use of blockchain technology and sophisticated AI techniques. Overall, this study highlights the importance of credit card theft detection and the promise of machine learning in mitigating this ubiquitous problem financial institutions use advanced machine learning algorithms and analytics function to detect fraudulent behaviour, protect customer interests, and maintain payment environment integrity to improve their capabilities.

ВИЯВЛЕННЯ ШАХРАЙСТВА З КРЕДИТНИМИ КАРТКАМИ МЕТОДАМИ МАШИННОГО НАВЧАННЯ

In the work, a study of large volumes of transactions was conducted. The problem of fraud detection is unique because it is necessary to take into account: data imbalance (ie, fraudulent transactions are usually less than 1% compared to normal ones); fraud scenarios change over time and need to be detected quickly; transactions usually contain numerous categorical characteristics; as a result of the confidentiality of transactions, there are no publicly available datasets. All this creates problems with the development of classification methods and with the selection of performance evaluation metrics. Threshold indicators for evaluating the effectiveness of classifiers were studied and the expediency of using thresholdless metrics was substantiated. There is currently no consensus on which set of performance indicators should be used. The primary analysis of research data was carried out and two techniques for eliminating class imbalance were applied: random undersampling, SMOTE technique. Removal of outliers was shown to improve the accuracy of the classification methods by more than 3%. A number of classifiers were built to determine fraudulent operations, statistical processing of the obtained results was carried out, which allowed to assess the adequacy of the built classifiers, to determine their optimal parameters, at which the classifiers work with maximum efficiency. It should be noted that all classifiers were tested on real data. Determined: The logistic regression classifier performs best on both the training and cross-validation sets. The Precision-Recall indicator was used to assess the effectiveness of the logistic regression model. For undersampling and oversampling, fully connected neural networks with one hidden layer were constructed and their accuracy was compared. It should be noted that the neural network on the oversampled data set predicts fewer correct fraud transactions than the model using the undersampled data set.

Counterfeit Product Detection using Blockchain Technology

A fraudulent practice where products that are passed off as genuine, product counterfeiting represents consumer fraud, which translates to financial losses. Transparency in supply chain management also faces a big challenge in terms of service repetition and lack of coordination. Numerous remedies have been implemented to battle counterfeiting, among them Artificial Intelligence and RFID, among others, all of which have significant issues such as incivility. This project tackles the problem by applying blockchain technology to improve counterfeit determination and assure product items’ authenticity all over the source chain. The scheme is built on blockchain encryption techniques, which promote a decentralized service for genuine authentication and duplication identification. Through the usage of QR codes and barcodes found in wireless technologies, counterfeit items are eradicated . When scanning a product’s QR or barcode with a camera scanner, the product’s unique code is linked to the blockchain, and all its complete details are stored within it. The database consists of multiple blocks, each one representing a unique product. In case of scanning, if the entered code matches with the database, a popup notification will display on the customer’s device that this product is genuine. Contrary to it, if there is no match found, means it is a counterfeit product; then a popup displays on the customer’s mobile, computer screen about the fraud transaction of the manufacturer from which points this scam is originating if the customer allows doing this action using the application . This will enable users to authenticate products independently without relying on sellers.

Model of Legal Protection in E-commerce Transactions to Improve the Community Economy in Indonesia

Increasingly rapid technological advances, today's transactions can purchase goods easily from their own homes through e-commerce electronic commerce. The aim of this research is to provide protection to the community if they experience problems and to help consumers in cases where there is a loss in e-commerce transactions in order to support the government in improving the economy of the community in Indonesia. Based on Law no. 8 of 1999 concerning consumer protection which applies to e-commerce transactions carried out via the internet as a platform for upholding consumer protection, this research uses a normative juridical approach by examining literature and written law to connect consumer legal protection models with real world situations. Goods transactions occur online, public spaces that are vulnerable to legal problems prevent business actors from committing fraud in electronic transactions because of the many platforms available. Consumer protection laws will also protect consumers and prevent mistakes in doing business.

Efficient loss updated XGBoost with deep emended genetic algorithm for detecting online fraudulent transactions

Efficient loss updated XGBoost with deep emended genetic algorithm for detecting online fraudulent transactions

An Optimized Deep Learning Approach for Detecting Fraudulent Transactions

An Optimized Deep Learning Approach for Detecting Fraudulent Transactions

TRANSFORMING FINTECH FRAUD DETECTION WITH ADVANCED ARTIFICIAL INTELLIGENCE ALGORITHMS

The rapid evolution of financial technology (fintech) platforms has exponentially increased the volume and sophistication of financial transactions, concurrently elevating the risk and complexity of fraudulent activities. This necessitates a paradigm shift in fraud detection methodologies towards more agile, accurate, and predictive solutions. This paper presents a comprehensive study on the transformative potential of advanced Artificial Intelligence (AI) algorithms in enhancing fintech fraud detection mechanisms. By leveraging cutting-edge AI techniques including deep learning, machine learning, and natural language processing, this research aims to develop a robust fraud detection framework capable of identifying, analyzing, and preventing fraudulent transactions in real-time.
 Our methodology encompasses the deployment of several AI algorithms on extensive datasets comprising genuine and fraudulent financial transactions. Through a comparative analysis, we identify the most effective algorithms in terms of accuracy, efficiency, and scalability. Key findings reveal that deep learning models, particularly those employing neural networks, outperform traditional machine learning models in detecting complex and nuanced fraudulent activities. Furthermore, the integration of natural language processing enables the extraction and analysis of unstructured data, significantly enhancing the detection capabilities.
 Conclusively, this paper underscores the critical role of advanced AI algorithms in revolutionizing fintech fraud detection. It highlights the superior performance of AI-based models over conventional methods, offering fintech platforms a more dynamic and predictive approach to fraud prevention. This research not only contributes to the academic discourse on financial security but also provides practical insights for fintech companies striving to safeguard their operations against fraud.
 Keywords: Artificial Intelligence, Fintech, Fraud Detection, Ethical Ai, Regulatory Compliance, Data Privacy, Algorithmic Bias, Predictive Analytics, Blockchain Technology, Quantum Computing, Interdisciplinary Collaboration, Innovation, Transparency, Accountability, Continuous Learning, Ethical Principles, Real-Time Processing, Financial Sector.

CREDIT CARD FRAUD DETECTION USING A STACKING ENSEMBLE APPROACH WITH LSTM AND RANDOM FOREST MACHINE LEARNING TECHNIQUES

Credit cards play an essential role in today’s digital economy, and their usage has recently grown tremendously, accompanied by a corresponding increase in credit card fraud. Machine learning (ML) algorithms have been utilized for credit card fraud detection. However, the dynamic shopping patterns of credit card holders and the class imbalance problem have made it difficult for ML classifiers to achieve optimal performance. This research project aims to develop a reliable credit card fraud detection system through a stacking ensemble method, integrating LSTM and Random Forest machine learning techniques. This approach aims to enhance fraud detection accuracy by leveraging the diverse strengths of both models. The system will undergo rigorous evaluation to ensure its efficiency in identifying fraudulent transactions while minimizing false positives. By combining the temporal sequencing capabilities of LSTM with the decision-making process of Random Forest, the proposed approach seeks to achieve heightened sensitivity to fraudulent patterns while maintaining computational efficiency. Ultimately, the objective is to bolster security measures and protect financial institutions and consumers from potential fraud risks.

Real Time Credit Card Fraud Detection and Reporting System Using Machine Learning

This study addresses the critical issue of real-time credit card fraud detection using machine learning. The primary goal is to develop a model that promptly identifies fraudulent transactions and alerts users. Two algorithms—Random Forest and Decision Tree Classifier were used, alongside various sampling techniques to balance the dataset and enhance performance. Six models were created, each with different accuracy levels in fraud detection. Key findings include a higher incidence of fraud among individuals over 75 years, likely due to less familiarity with modern transaction methods. Additionally, a majority of transactions involved females, indicating a potential higher fraud risk in these transactions. The Random Forest -SMOTE [Hyperparameter Tuned] model was the most effective, achieving a 97% accuracy rate, 95% F1 score, and 98% precision rate. For practical application, this model was integrated with Twilio for real-time fraud alerts, proving successful in sending timely, accurate notifications. The study highlights valuable insights and a robust solution for real-time fraud detection and response. Regular performance evaluations of the model are recommended to maintain its effectiveness against evolving fraud patterns.

Mobile Forensics in Financial Fraud Analysis

Mobile forensics, a critical component of digital forensics, is essential for investigating and analyzing digital evidence pertinent to financial fraud security. This guide delineates key steps and methodologies, with a focus on device identification, secure data extraction, password decryption, cloud and deleted data analysis, timeline reconstruction, network and geolocation analysis, app and social media investigations, and comprehensive reporting. The operational process involves acquiring mobile devices, analyzing file systems for financial data, extracting and decoding relevant information, verifying findings, collaborating with network forensics for broader insights, and presenting conclusive results. Mobile forensics plays a pivotal role in uncovering evidence for financial crimes, ranging from fraudulent transactions to data breaches, where mobile devices serve as critical tools. Specialized techniques in mobile forensics contribute significantly to meticulous and successful investigations aimed at mitigating financial fraud risks. Keywords- Mobile Forensics, Data, Analysis, Extraction, Evidence, Financial fraud

A Comprehensive Approach to Safeguard Credit Card Transactions and Fraud Prevention

The escalating prevalence of financial fraud within the financial sector poses profound challenges. Detecting credit card fraud in online transactions necessitates data mining due to inherent complexities. Addressing two key issues—evolving patterns in legitimate and fraudulent behaviors and highly skewed datasets of credit card frauds—renders the task challenging. This paper scrutinizes the performance of naive Bayes, KNN, and logistic regression on significantly imbalanced credit card fraud data comprising 284,807 transactions from European cardholders. The dataset's skewness is addressed through a hybrid under-sampling and oversampling approach. The three techniques are applied to both unprocessed and preprocessed data. Fraud detection, defined as a set of activities thwarting illicit acquisition of assets or funds through deceptive means, varies across industries and methods. Credit card fraud, particularly susceptible due to its ease and prevalence in e-commerce and online platforms, prompted the adoption of diverse machine learning strategies to combat rising fraud rates. This paper employs machine learning algorithms for credit card fraud detection, utilizing a publicly available credit card dataset for model evaluation. While acknowledging that achieving 100% accuracy in fraud detection is elusive, the paper emphasizes the real-world applicability of its findings through the analysis of credit card data from a financial institution. In addition to assessing model efficacy, the study introduces noise into the data samples to evaluate algorithm robustness. Experimental outcomes underscore the effectiveness of the majority voting method, achieving commendable accuracy rates in detecting credit card fraud cases. The study sheds light on the pressing issue of credit card fraud, emphasizing the importance of deploying robust machine learning approaches for timely and accurate detection in real-world scenarios.

Implementation of Credit Card Fraud Detection

The issue of credit card fraud presents a notable concern within the financial sector, leading to considerable finacial losses for both financial institutions and consumers. To address this challenge, this study investigates the application of machine learning methods for detecting credit card fraud. We explore the performance of diverse machine learning algorithms on an actual dataset and suggest an ensemble-based approach that harnesses the strengths of multiple models. Our experimental outcomes demonstrate the effectiveness of machine learning in accurately identifying fraudulent transactions while minimizing false positives.

Explainable AI in Credit Card Fraud Detection: Interpretable Models and Transparent Decision-making for Enhanced Trust and Compliance in the USA

Credit Card Fraud presents significant challenges across various domains, comprising, healthcare, insurance, finance, and e-commerce. The principal objective of this research was to examine the efficacy of Machine Learning techniques in detecting credit card fraud. Four key Machine Learning techniques were employed, notably, Support Vector Machine, Logistic Regression, Random Forest, and Artificial Neural Network. Subsequently, model performance was evaluated using Precision, Recall, Accuracy, and F-measure metrics. While all models demonstrated high accuracy rates (99%), this was largely due to the dataset's size, with 284,807 attributes and only 492 fraudulent transactions. Nevertheless, accuracy solely did not provide a comprehensive comparison metric. Support Vector Machine showed the highest recall (89.5), correctly identifying the most positive instances, highlighting its efficacy in detecting true positives. On the other hand, the Artificial Neural Network model exhibited the highest precision (79.4, indicating its capability to make accurate identifications, making it proficient in optimistic predictions.

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.

Credit Card Fraud Detection

The issue of credit card fraud presents a notable concern within the financial sector, leading to considerable financial losses for both financial institutions and consumers. To address this challenge, this study investigates the application of machine learning methods for detecting credit card fraud. We explore the performance of diverse ma- chine learning algorithms on an actual dataset and suggest an ensemble-based approach that harnesses the strengths of multiple models. Our experimental outcomes demon- strate the effectiveness of machine learning in accurately identifying fraudulent transactions while minimizing false positives.

Fraud Feature Boosting Mechanism and Spiral Oversampling Balancing Technique for Credit Card Fraud Detection

With the flourishing of the credit card business and Internet technology, the risk of fraudulent credit card transactions is ever-increasing due to the complex information involved in the credit card business. Since the high redundancy of feature information and imbalance of class distribution in transaction data, the performance of the existing machine learning-based models for detecting credit card fraudulent transactions still needs to be improved. Therefore, it is crucial to build fraud detection models for effective feature engineering and sampling techniques. This article proposes a credit card fraud detection model incorporating a fraud feature-boosting mechanism with a spiral oversampling balancing technique (SOBT). Specifically, we present a compound grouping elimination strategy to exclude highly redundant and correlated features from the credit card transaction dataset and improve the data quality. Furthermore, we design a multifactor synchronous embedding mechanism, which combines the performance evaluation metrics of the embedding model for each feature and improves the decision-making ability of each feature for the target domain. Moreover, we propose an SOBT to balance the ratio of legitimate to fraudulent transactions, which improves the ability of the fraud detection model to distinguish legitimate from fraudulent transactions. Extensive experimental results based on two real-world datasets demonstrate that our methods can facilitate efficient credit card fraud detection and achieve better performance than state-of-the-art methods.

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.

A Multiperspective Fraud Detection Method for Multiparticipant E-Commerce Transactions

Detection and prevention of fraudulent transactions in e-commerce platforms have always been the focus of transaction security systems. However, due to the concealment of e-commerce, it is not easy to capture attackers solely based on the historic order information. Many works try to develop technologies to prevent frauds, which have not considered the dynamic behaviors of users from multiple perspectives. This leads to an inefficient detection of fraudulent behaviors. To this end, this article proposes a novel fraud detection method that integrates machine learning and process mining models to monitor real-time user behaviors. First, we establish a process model concerning the business-to-customer (B2C) e-commerce platform, by incorporating the detection of user behaviors. Second, a method for analyzing abnormalities that can extract important features from event logs is presented. Then, we feed the extracted features to a support vector machine (SVM)-based classification model that can detect fraud behaviors. We demonstrate the effectiveness of our method in capturing dynamic fraudulent behaviors in e-commerce systems through the experiments.