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

Naveen Kumar Kokkalakonda *

Independent Researcher, USA.
 
Research Article
International Journal of Science and Research Archive, 2022, 07(01), 564-575.
Article DOI: 10.30574/ijsra.2022.7.1.0248
Publication history: 
Received on 25 September 2022; revised on 25 October 2022; accepted on 28 October 2022
 
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.
 
Keywords: 
AI-powered fraud detection; Machine learning algorithms; Banking security; Neural networks; Real-time processing; Fraud mitigation; Data management
 
Full text article in PDF: 
 
Copyright information: 

Copyright © 2022 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0