HAMI-AML-DETECTOR

HAMI AML Detector 🕵️‍♂️🚨

Overview

HAMI AML Detector is a powerful graph neural network (GNN) based solution designed for real-time Anti-Money Laundering (AML) transaction monitoring. The model dynamically builds transaction graphs and detects suspicious transaction patterns such as:

• Fan-In
• Fan-Out
• Cycle
• Scatter-Gather
• Gather-Scatter
• and more...

How it Works

1. Transaction Graph Construction

   • Transactions are represented as nodes and edges in real-time.
   • Dynamically clusters transactions based on shared accounts.

2. Graph Attention Network (GAT)

   • Uses attention mechanisms to learn important transaction patterns.
   • Classifies each transaction cluster as "AML" or "Normal."

3. Real-Time Monitoring

   • Integrates seamlessly with Kafka for real-time AML detection.
   • Continuously updates and evaluates transaction networks.

Intended Use

• Real-time fraud and AML monitoring by financial institutions.
• Enhanced accuracy in identifying and mitigating financial crimes.

Frameworks & Technologies

• PyTorch
• PyTorch Geometric
• Kafka (for real-time integration)
• NetworkX (for graph management)

Performance

The model shows excellent capability in detecting sophisticated AML patterns with high accuracy on simulated transaction datasets. A full performance analysis including confusion matrices and accuracy metrics is provided in the repository.

How to use the model

Please visit the repository for detailed instructions:

git clone https://huggingface.co/Ymak7/HAMI-AML-DETECTOR