The Role of Machine Learning in Financial Fraud Detection: A Comparative Study

Abstract

With the increasing digitization of financial transactions, fraud detection has become a critical area of concern for financial institutions. This paper investigates the role of machine learning (ML) algorithms in detecting financial fraud, comparing traditional rule-based systems with ML-driven approaches. Through case studies of three leading financial institutions in the UK, we assessed the effectiveness of ML models in identifying suspicious activities, reducing false positives, and enhancing detection accuracy. The findings reveal that ML-based systems significantly outperform traditional methods in real-time fraud detection, though challenges such as data privacy and algorithmic bias remain. The paper concludes with recommendations for integrating advanced ML techniques with regulatory compliance to strengthen financial fraud prevention.