Real-Time Fraud Detection at Scale: An Architectural Framework for FinTech Big Data Systems

Abstract

Digital finance platforms generate massive streams of transactions every second. Alongside convenience and scale, this environment has also enabled more intricate forms of financial fraud. Many institutions still rely on batch analytics that review transactions only after they occur, making timely intervention difficult and increasing exposure to losses and regulatory scrutiny. This study examines structural weaknesses in existing Big Data infrastructures used for fraud detection in FinTech and proposes a framework designed to flag suspicious activity as transactions unfold. The model integrates stream processing, machine-learning–based pattern analysis, and distributed data storage to manage high-volume transaction flows. Tests using simulated workloads and anonymized banking transaction logs show faster detection, higher processing capacity, and improved identification of fraudulent activity, indicating strong potential for deployment in large financial systems.