AI-Enhanced Network Traffic Analysis for Preventing Fraud Payment in Banks

Volume 18, Number 2

AI-Enhanced Network Traffic Analysis for Preventing Fraud Payment in Banks

Authors

Balavardhan Reddy¹ and Amir Ahmed Ansari², ¹University of the Cumberlands, USA, ²Indiana Wesleyan University, USA

Abstract

The expansion in the use of digital banking, mobile payment services, and online financial services has put tremendous pressure on banking networks, making them vulnerable to sophisticated payment fraud schemes. The inflexibility of rule-based systems in detecting sophisticated payment fraud has led to the need for smarter solutions to detect payment fraud in banking networks. This study presents a framework for artificial intelligence-based network traffic analysis to detect payment fraud in banking networks. The proposed framework utilizes artificial intelligence to process vast amounts of data regarding network activities in real-time to detect payment fraud in banking networks. The study presents artificial intelligence-based network traffic analysis to detect payment fraud in banking networks. The proposed framework utilizes artificial intelligence to process vast amounts of data regarding network activities in real-time to detect payment fraud in banking networks. The proposed framework has been found to improve the accuracy of payment fraud detection in banking networks compared to rule-based systems. The proposed framework has been found to improve the accuracy of payment fraud detection in banking networks compared to rule-based systems.

Keywords

In the digital finance domain, AI is responsible for intelligent network traffic analysis, which increases payment fraud detection and improves banking cybersecurity. Machine learning is the basis for anomaly detection, providing financial security and improving digital banking fraud prevention.