Hybrid Machine Learning Models for Secure Banking Fraud Identification

Abstract

The detection of bank misconduct is more critical than ever as the number of digital activities increases. In a world where cyber threats are evolving at a dizzying rate, machine learning (ML) has emerged as a powerful tool for detecting suspicious activity. In this study, we examine how businesses can rapidly detect fraud by analyzing massive datasets with machine learning techniques such as random forests, decision trees, neural networks, and support vector machines. Machine learning (ML) enhances accuracy, decreases false positives, and speeds up reaction time through feature selection, preprocessing, and model performance review. Ultimately, machine learning facilitates the protection of consumer transactions, the elimination of fraud, and the improvement of banking security and usability.