STEPHEN AI-Powered Financial Compliance System

Financial institutions today are overwhelmed by legacy compliance systems that generate up to 95% false positives, leading to operational fatigue and missed threats. STEPHEN (Senior Technical Heuristic & Predictive Intelligence Network) is a production-ready solution designed for the Deriv AI Talent Sprint that shifts compliance from a reactive discipline to a predictive one. At its core, STEPHEN utilizes a Hybrid Intelligence Architecture. It combines a deterministic Logic Engine for regulatory thresholds with an unsupervised Isolation Forest model for anomaly detection. Furthermore, it employs Graph Theory (NetworkX) to identify and flag circular transaction patterns, commonly used in "smurfing" laundering schemes. Our Predictive Risk Model V4 utilizes linear regression to forecast future activity, identifying potential threshold breaches days before they occur. Beyond detection, STEPHEN leverages Google Gemini to bridge the gap between complex data and human analysts. The integrated AI Agent provides natural language querying capabilities (NL-to-SQL), allowing investigators to ask questions like "Show me high-risk cash transactions" without writing a single line of code. Additionally, the Regulatory Radar scans RSS feeds from agencies like FinCEN, using GenAI to parse legal text and automatically propose system rule updates based on new laws. Built with "Privacy by Design," STEPHEN features a robust PII Masker that redacts sensitive data (emails, names, phone numbers) before it reaches the cloud, and an immutable, cryptographically chained audit ledger to ensure every AI and human decision is tamper-proof. This end-to-end platform represents the future of scalable, secure, and intelligent financial defense for global institutions.