Safeguarding against Cyber Threats: Machine Learning-Based Approaches for Real-Time Fraud Detection and Prevention †

The proliferation of internet services in various industries, especially the financial sector, has increased financial fraud. Fraud detection and prevention are critical to protecting both individuals and organizations from significant financial loss. However, the lack of publicly available datasets containing fraud is a major challenge. This study aims to address these issues using advanced machine learning techniques. Known for their ability to provide insight into data, decision trees are used for real-time fraud detection. In addition, deep learning techniques and artificial neural networks (ANN) are used to detect complex fraud patterns, while logistic regression is used to model the probability of fraudulent events. The accuracy of these methods, including decision trees, logistic regression, and ANN, is fully evaluated, with accuracies of 99.8%, 99.9%, and 99.94%, respectively. These findings provide valuable guidance for companies on choosing effective anti-fraud strategies and shed light on the adaptability of algorithms to real financial contexts, contributing to machine learning-based fraud detection.