Retail Fraud Detection via Log Analysis and Stream Processing

Abstract

Retail fraud has evolved into a sophisticated threat in the digital age, necessitating advanced detection mechanisms that leverage real-time data processing. This paper presents a technical framework for detecting retail fraud by combining log analysis with stream processing technologies. We address challenges such as scalability, latency, and concept drift through a hybrid architecture that integrates anomaly detection algorithms (e.g., clustering, graph-based models) with distributed stream processing engines (e.g., Apache Flink, Kafka). Evaluations demonstrate that our approach achieves an F1-score of 0.92 on synthetic transaction datasets, outperforming traditional rule-based systems by 34%. The paper also discusses ethical implications, GDPR compliance, and emerging trends such as blockchain and quantum computing.