FraudShield AI Real-Time Fraud Detection

FraudShield AI tackles the $32+ billion annual fraud problem in financial services with an intelligent, real-time detection platform. The Problem Traditional rule-based systems have 80-90% false positive rates, frustrating customers and overwhelming analysts. Modern regulations (EU AI Act, GDPR) demand explainable AI decisions, not black-box models. Our Solution We built an ensemble machine learning system combining three complementary approaches: - Isolation Forest (30%) - Catches novel, unknown fraud patterns through unsupervised anomaly detection - XGBoost (50%) - High-accuracy supervised classification on known fraud types - Heuristic Rules (20%) - Domain-expert rules for regulatory compliance and interpretability Key Features - Real-time Analysis: Sub-100ms scoring per transaction - Explainable AI: Every decision includes natural language explanations ("Large $45,000 trade at 2:34 AM with velocity burst detected") - Risk Scoring: Continuous 0-100 scores instead of binary fraud/not-fraud - Human-in-the-Loop: Analysts can confirm or dismiss alerts, feeding back into model improvement - 15+ Fraud Signals: Velocity attacks, amount anomalies, off-hours trading, account takeover patterns, and more Technical Stack - Backend: FastAPI (Python) with scikit-learn and XGBoost - Frontend: Next.js 14 with real-time dashboard - Deployment: Vercel (frontend) + Render (backend) Impact Our calibrated system targets realistic fraud rates (1-5%), dramatically reducing false positives while maintaining high recall on actual fraud. The explainability layer ensures compliance with emerging AI regulations while building analyst trust. Live demo available, experience real-time fraud detection with synthetic trading data.