AI-powered fraud detection in banking: enhancing security with machine learning algorithms

Abstract

Modern banking security measures have become essential due to progressive advances in banking fraud schemes. The study explores the use of AI-driven fraud detection technology for analyzing machine learning systems which detect and fight fraudulent banking transactions. The study uses decision trees and random forests alongside support vector machines and neural networks to measure their results on accuracy and response time as well as precision and recall. Results demonstrate that neural networks provide superior performance to other models since they detect fraud with 96.1% accuracy at a response time of 32 ms. The research demonstrates the security effects of AI implementation which involves decreasing false responses while providing risk management solutions for data reliability and fraud pattern changes. The continuing research activates target feature enhancement together with ensemble methods implementation and blockchain integration for secure transparent data system management. The conducted research demonstrates how AI holds immense power to develop banking security systems which remain powerful while being efficient and adaptable.