AI-Driven Fraud Detection Systems

Abstract

Financial fraud costs the global economy an estimated 5.4 trillion USD annually, and the sophistication of fraud schemes continues to outpace traditional rule-based detection systems. AI-driven approaches offer the ability to learn complex, evolving fraud patterns from transaction data, but face unique challenges including extreme class imbalance (fraud rates below 1%), adversarial adaptation by fraudsters, real-time latency requirements, and the need for explainable alerts to support human investigation. This study presents a controlled evaluation of six fraud detection approaches -- XGBoost (tabular baseline), LSTM sequence models, graph neural networks for transaction networks, autoencoders for anomaly detection, ensemble stacking, and online learning with concept drift adaptation -- across three fraud domains: payment card fraud (IEEE-CIS dataset, 590K transactions, 3.5% fraud rate), insurance claims fraud (synthetic realistic dataset, 250K claims, 6.2% fraud rate), and account takeover detection (login behaviour dataset, 180K sessions, 1.8% attack rate). A total of 2,160 experiments were conducted under temporal evaluation protocols that simulate real deployment. XGBoost achieved the highest recall at 1% false positive rate on payment card fraud (0.648 +- 0.018), confirming tree-based methods as the production standard. LSTM sequence models improved recall to 0.684 +- 0.022 by capturing temporal transaction patterns (velocity, merchant sequence) that single-transaction features miss. Graph neural networks detected 28.4% more fraud ring activity than non-graph methods by modelling transaction network topology. Online learning with drift detection maintained stable performance over a 12-month simulated deployment where static models degraded by 14.8% due to evolving fraud patterns. The false-positive investigation cost was the dominant operational expense: reducing the false positive rate from 2% to 1% saved an estimated 4.2M USD annually for a mid-sized payment processor. A practical fraud detection pipeline selection guide mapping fraud type, data availability, and latency requirements to recommended configurations is proposed