Develop an EvoAML framework integrating graph networks and temporal evolution analysis, aimed at addressing cross-industry tracking gaps and translating these methods into BSA/AMLA 2020 compliant solutions.

A free cryptowallet risk scoring tool with fully explainable scoring.

Open source AML and Fraud Detection using Machine Learning for Real-Time Transaction Monitoring

Anti Money Laundering Detection using Graph Attention Network

Using GCN/GAT with keras to detect money laundering in the Bitcoin Blockchain, using the Elliptic Dataset.

A Multi-Agent RAG platform using Vector DBs and AI models to accurately answer AML/CFT compliance queries.

This repo demonstrates a bank’s transaction clearing process, utilizing generative AI for real-time fraud detection and AML compliance, enhancing the detection of evolving fraudulent techniques.

AML Checker detection and analysis tools for cryptocurrency transactions using machine learning and blockchain data. Ensuring compliance and reducing money laundering risks.

Official AMLTRIX Data Exports – Versioned STIX 2.1 bundles, CSVs, XLSX files, and ATT&CK Navigator layers representing the latest AMLTRIX knowledge graph of adversarial behaviors in financial crime. See framework.amltrix.com for methodology and usage.

TNO PET Lab - secure Multi-Party Computation (MPC) - Protocols - Secure Risk Propagation

Reference implementation of ERC-8047 Forensic Token (Forest)

Crypto AML Vendor Evaluation Checklist & Implementation Guide（Crypto AML 供应商评估 Checklist 与执行指南）

Personal take on GraphDB + AML with AWS Neptune + Glue + Lambda.

A production-ready AML compliance platform that uses machine learning and AI to detect financial crimes in real-time. Built with microservices architecture, it analyzes transaction patterns, screens against sanctions lists, and automatically generates regulatory reports using OpenAI integration.

Name Matching ML model for entity resolution and transaction monitoring

Real-time Mule Account Detection Engine for UPI. Uses Graph Neural Networks (GraphSAGE), JA3 Fingerprinting, and Behavioral Forensics (EIF) to identify money laundering rings, smurfing, and collusive fraud networks in milliseconds.

Utilize the machine Learning to classify transactions as fraudulent(1) or non-fraudulent(0).

XmlGoaml is a .NET project designed for processing XML data with advanced validation against XSD schemas, specifically targeting reports related to financial intelligence and suspicious activity reporting, aligned with the goAML standards.

玉山人工智慧公開挑戰賽2020夏季賽 - NLP應用挑戰賽