AI-Driven Real-Time Financial Fraud Detection with Explainable Machine Learning

Abstract

Digital financial fraud has intensified in step with the global proliferation of online payment channels, mobile banking, and contactless transactions. Conventional rule-based detection engines reliant on static thresholds and hand-coded heuristics cannot adapt quickly enough to the pace at which fraud patterns evolve, producing high false-positive rates and leaving novel attack vectors undetected for extended periods. This paper presents a production-grade, AIdriven fraud detection platform that combines five machine learning classifiers Logistic Regression, Random Forest, XGBoost, LightGBM, and Isolation Forest to score financial transactions in real time. XGBoost is designated the primary production model, achieving a ROC-AUC of 60.40% on a synthetic dataset of 50,000 transactions exhibiting a 5.69% fraud rate. SMOTE addresses training-set class imbalance. SHAP values accompany every prediction, translating opaque model outputs into plain-language, per-feature justifications satisfying regulatory transparency requirements. The FastAPI backend processes each transaction in approximately 35 ms end-to-end and sustains throughput in excess of 28 transactions per second. A Next.js 14 analyst dashboard renders a live transaction feed, Recharts-based analytics, and a tiered alert panel.