An Introduction to Machine Learning Methods for Fraud Detection

Financial fraud represents a critical global challenge with substantial economic and social consequences. This comprehensive review synthesizes the current knowledge on machine learning approaches for financial fraud detection, examining their effectiveness across diverse fraud scenarios. We analyze various fraud types, including credit card fraud, financial statement fraud, insurance fraud, and money laundering, along with their specific detection challenges. The review outlines supervised, unsupervised, and hybrid learning approaches, discussing their applications and performance in different fraud detection contexts. We examine commonly used datasets in fraud detection research and evaluate performance metrics for assessing these systems. The review is further grounded by two case studies applying supervised models to real-world banking data, illustrating the practical challenges of implementing fraud detection systems in operational environments. Through our analysis of the recent literature, we identify persistent challenges, including data imbalance, concept drift, and privacy concerns, while highlighting the emerging trends in deep learning and ensemble methods. This review provides valuable insights for researchers, financial institutions, and practitioners working to develop more effective, adaptive, and interpretable fraud detection systems capable of operating within real-world financial environments.