Efficient and Scalable Bank Fraud Detection with Machine Learning Algorithms

Abstract

Bank fraud detection is a critical area of concern in the financial sector, with fraudulent activities causing substantial financial losses globally. Traditional rule-based systems for fraud detection, while effective to some extent, have proven inadequate in dealing with increasingly sophisticated fraud schemes. Machine Learning (ML) offers a promising solution by enabling systems to detect patterns and anomalies in real-time transactions with high accuracy. This research explores various machine learning algorithms and their applications in developing scalable and efficient bank fraud detection systems. We provide an in-depth analysis of different supervised and unsupervised learning methods, discussing their strengths, challenges, and scalability for real-world implementation in financial institutions.