EvoAML: Evolutionary Anti-Money Laundering RegTech Framework
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Overview
EvoAML is an open-source RegTech framework designed to bridge the gap between advanced AI research and practical anti-money laundering compliance. It provides a modular pipeline for detecting complex money laundering patterns across cross-industry financial networks.
Core Architecture
EvoAML integrates multiple detection methodologies to provide comprehensive coverage:
-
Graph-Driven Cross-Industry Tracking [In Progress]
Uses graph-based analysis to trace financial flows across supply chains and multi-sector networks.
-
Temporal Evolution Analysis [In Progress]
Applies time-series analysis to identify evolving behavioral patterns indicative of laundering.
-
AMLA 2020 Compliance Engine [Implemented]
Translates detection results into standardized BSA-compliant SAR (Suspicious Activity Report) narratives.
Quick Start
- Install dependencies:
pip install -r requirements.txt
- Run the dashboard:
streamlit run app.py
- See
examples/ for algorithmic workflows.
Roadmap
- Phase 1: Foundation & Compliance Architecture ✅
- Phase 2: Data Ingestion & Preprocessing ✅
- Phase 3: Graph Tracking Module ✅
- Phase 4: Temporal Analysis Module ✅
- Phase 5: Compliance Engine ✅
- Phase 6: Visualization & Release ✅
EvoAML accepts transaction data in CSV format. The following columns are required:
sender (string): Source entity identifierreceiver (string): Destination entity identifieramount (float): Transaction amount in USD
Optional columns:
sector (string): Industry sector (e.g., Energy, Finance, Logistics)timestamp (string): Transaction date (ISO format preferred)
Example CSV
sender,receiver,amount,sector,timestamp
Entity_001,Entity_002,50000,Energy,2024-01-15
Entity_003,Entity_001,25000,Finance,2024-01-16
Entity_004,Entity_005,75000,Logistics,2024-01-17
You can upload this CSV directly in the Streamlit dashboard to run the analysis on your own data.
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