Integration of Machine Learning Techniques in Bank Fraud Detection and Prevention

The rapid expansion of digital banking services has significantly increased the exposure of financial institutions to various forms of fraud, including payment fraud, identity theft, and account takeover. Traditional rule-based fraud detection systems are increasingly ineffective in managing high-volume, high-velocity transactional data and adapting to evolving fraud patterns. In this context, machine learning (ML) has emerged as a key technological solution for enhancing fraud detection and prevention capabilities. This article examines the application of machine learning techniques in bank fraud detection, with a focus on supervised, unsupervised, and hybrid approaches. Supervised models leverage labeled historical data to identify known fraud patterns, while unsupervised methods detect anomalies in unlabeled datasets. Hybrid approaches combine both strategies to improve robustness and adaptability. The analysis draws on recent academic literature and practical implementations within the banking sector. The results indicate that ML-based systems are efficient in identifying anomalous transactions, reducing false positives, and improving overall operational efficiency. Additionall