Automating fraud prevention in credit and debit transactions through intelligent queue systems and regression testing

Abstract

The rapid increase in digital financial transactions has intensified the need for robust fraud prevention mechanisms, especially in credit and debit card transactions. Traditional methods, while effective to an extent, often fall short in identifying complex, evolving fraud patterns. This paper explores the automation of fraud prevention using intelligent queue systems and regression testing, presenting an innovative approach that adapts to real-time transaction analysis. Intelligent queue systems prioritize transaction monitoring based on risk assessments derived from machine learning algorithms, ensuring that high-risk transactions are reviewed promptly and efficiently. Regression testing, meanwhile, serves as a continual validation tool, simulating various fraud scenarios to verify the system's accuracy in flagging fraudulent activities. By integrating these two components, the proposed model offers a dynamic, adaptive framework for detecting and preventing fraud, minimizing false positives, and optimizing transaction flow. This automation reduces manual intervention and operational costs, while maintaining high standards of transaction security. Results from case studies and simulations indicate that this approach can enhance fraud detection rates, streamline processing, and contribute to a more secure financial ecosystem.