Multi-Layered Fraud Detection System in E-commerce Using Machine Learning and Behavioural Analytics

Abstract - With the rapidly emerging e-commerce environment, particularly for the auto-spare parts market, the traditional fraud detection paradigms have become increasingly unsuitable with high-flying fraud situations and real-time transactional environments. In this paper, an integrated multi-modal fraud detection system is presented comprising machine learning, behavior analysis, and real-time surveillance for providing an effective security system. The system employs a Random Forest Classifier along with behavior pattern analysis for detecting suspicious user behavior and attempted fraud. With the inclusion of supervised learning along with real-time monitoring of behavior, the system is more precise in detection along with lowering false positives. The setup comprises monitoring of user behavior from session monitoring, IP address checks, and click pattern detection for ensuring comprehensive fraud prevention coverage. The system architecture comprises real-time alerting mechanisms and an instant response administrative dashboard. Preliminary results indicate significantly enhanced fraud detection accuracy along with lowering response times compared to traditional single-modal methods.