Abstract: Fraud detection has emerged as a vital area of research in the era of digitalization, where financial transactions and online services have become increasingly vulnerable to fraudulent activities. With the expansion of e-commerce, online banking, insurance claims, and telecommunication services, identifying and preventing fraud has become a major challenge for organizations. Traditional rule-based systems, while effective for structured and historical data, often fail to detect new and adaptive fraud patterns. As a result, modern fraud detection systems increasingly rely on advanced data-driven approaches such as Data Mining, Machine Learning, Deep Learning and Artificial Intelligence to recognize suspicious behavior and anomalies in real time.

A comprehensive review of current fraud detection techniques, including supervised learning, unsupervised learning, and hybrid models combine these approaches. It highlights widely used algorithms such as Decision Trees, Random Forests, Neural Networks, Support Vector Machines, and anomaly detection frameworks. Furthermore, it discusses key performance metrics like Precision, Recall, and ROC-AUC, which are essential for evaluating detection efficiency. It also addresses major challenges such as data imbalance, privacy concerns, lack of labeled datasets, and the dynamic nature of fraud schemes. Finally, it outlines emerging research trends focused on explainable Artificial Intelligence (AI), Graph-based Detection, and adaptive learning systems, offering insights into future pathway for building more accurate and resilient fraud detection mechanisms.

Keywords: Fraud Detection, Deep Learning, Graph Neural Network (GNN), Real-Time Credit Card Fraud, Banking Security, Artificial Intelligence.

Downloads: | DOI: 10.17148/IARJSET.2025.121217