Improving Bank Fraud Detection Efficiency through Machine Learning:

Abstract

The increasing sophistication of financial fraud has prompted banks to seek more effective methods for detecting and preventing fraudulent activities. Traditional fraud detection systems often struggle with high false positives and limited adaptability. This paper explores the application of machine learning (ML) techniques to enhance the efficiency and accuracy of bank fraud detection systems. We review various ML algorithms, including supervised and unsupervised learning methods, and their impact on reducing false positives and improving detection rates. Our analysis incorporates case studies and empirical data to evaluate the effectiveness of these techniques in real-world scenarios. We conclude with recommendations for integrating ML solutions into existing systems and future research directions to address current limitations.