A Hybrid Machine Learning Framework for Real-Time Financial Transaction Fraud Detection

Abstract

Digital Transactions have certainly made our life easier, but at the same time it makes us susceptible to many threats including misuse of UPI, fraudulent refund, phishing, account hacking, and many others. The traditionalrule-based system works according to predefined rules and is unable to cope with changing fraud trends, large number of transactions, false positives, and delay in the detection process. In this paper, we present Transaction Guardian, a real-time domain independent digital fraud detection system, where rule-based fraud engines, machine learning, behavioral analysis, and real time monitoring dashboard are employed. The designed system leverages Random Forests for supervised fraud detection, Isolation Forest for unsupervised anomaly detection along with velocity and geospatial analysis for the detection of burst transactions and impossible travel. Machine Learning module is exposed as a Flask service integrated with the Node.js/Express backend, MySQL for persistent storage, and React/TypeScript frontend. Real time communication is handled using Socket.IO library. Experiments carried out using 30,000 anonymized historical transaction dataset and live transaction feed prove that the proposed solution is capable of operating in real time, has reliable performance, high fraud-detection efficiency, and lower false positives