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
Transaction Graph Construction
- Transactions are represented as nodes and edges in real-time.
- Dynamically clusters transactions based on shared accounts.
Graph Attention Network (GAT)
- Uses attention mechanisms to learn important transaction patterns.
- Classifies each transaction cluster as "AML" or "Normal."
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
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