EvoAML: Evolutionary Anti-Money Laundering RegTech Framework

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🎯 Proposed Endeavor

To develop and advance a smart AML detection framework (EvoAML) integrating graph network cross-industry monitoring and temporal behavior evolution analysis, aimed at bridging the systemic technological gap in current anti-money laundering infrastructure regarding cross-industry fund flow tracking and dynamic money laundering pattern recognition, and to translate this methodology into a deployable solution compliant with U.S. regulatory frameworks (BSA/AMLA 2020).

💡 Core Architecture

EvoAML bridges the gap between state-of-the-art AI research and practical, compliant RegTech solutions.

1. Graph-Driven Cross-Industry Tracking: Employs Graph Neural Networks (GNN) to trace obfuscated money trails across complex supply chains and energy mobility sectors.
2. Temporal Evolution Analysis: Utilizes Dynamic Self-Attention Networks to predict and identify mutating money-laundering behaviors over time.
3. AMLA 2020 Compliance Engine: Automatically translates anomalous AI detections into standardized Bank Secrecy Act (BSA) narrative templates for Suspicious Activity Reports (SARs).

📅 Project Roadmap (6-Phase Plan)

For detailed technical breakdowns, see development_phases.md.

• Phase 1: Foundation & Compliance Architecture Setup ✅ (Current)
• Phase 2: Heterogeneous Data Ingestion & Preprocessing
• Phase 3: Graph-Driven Tracking Integration (GNN Module)
• Phase 4: Temporal Behavior Evolution Analysis
• Phase 5: BSA/AMLA 2020 Compliance Engine & SAR Generation
• Phase 6: System Simulation, Visualization Dashboard & v1.0 Release

🚀 Quick Start

See the examples/ directory for demonstrations of cross-industry tracking and automated SAR generation workflows.