MACHINE LEARNING: A GAME CHANGER IN FRAUD DETECTION STRATEGIES

ABSTRACT Fraud poses a persistent and rapidly evolving threat across financial services, e-commerce, insurance, telecommunications, and digital banking, leading to substantial economic losses worldwide. Traditional rule-based detection systems, while effective for known fraud patterns, struggle to adapt to the increasingly sophisticated and dynamic tactics employed by fraudsters. In response, machine learning (ML) has emerged as a powerful and adaptive tool for fraud detection, capable of identifying subtle, nonlinear, and previously unseen patterns in large, complex datasets. This paper examines the effectiveness of machine learning in fraud detection by reviewing key algorithms, performance metrics, application areas, and practical deployment challenges. Overall, the study concludes that machine learning significantly improves fraud detection effectiveness compared to traditional approaches. By enabling real-time, adaptive, and data-driven detection, ML enhances both accuracy and operational efficiency. Continued innovation in model interpretability, data management, and real-time learning will be crucial for maximizing ML’s long-term impact in combating modern fraud.