Advanced Rule-Based Fraud Detection Systems In Payment Processing

Abstract

Rule-based fraud detection systems represent an innovative approach to safeguarding payment transactions by applying predefined criteria to identify anomalies in real-time. This article investigates recent advancements in these systems, building on behavioral analysis, geofencing, and scoring mechanisms to mitigate risks in retail and e-commerce. Novel contributions include hybrid models integrating machine learning for adaptive rules, enhancing detection accuracy while minimizing false positives. Comparative insights reveal superior performance against traditional methods through explainable decisions and contextual evaluation. Potential applications span financial institutions and merchants, fostering trust in digital payments and addressing economic stability concerns in an increasingly complex transaction landscape.