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--- |
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license: mit |
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datasets: |
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- Ymak7/transactional-data |
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language: |
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- en |
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--- |
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# HAMI AML Detector 🕵️♂️🚨 |
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## Overview |
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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: |
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- **Fan-In** |
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- **Fan-Out** |
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- **Cycle** |
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- **Scatter-Gather** |
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- **Gather-Scatter** |
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- and more... |
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## How it Works |
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1. **Transaction Graph Construction** |
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- Transactions are represented as nodes and edges in real-time. |
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- Dynamically clusters transactions based on shared accounts. |
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2. **Graph Attention Network (GAT)** |
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- Uses attention mechanisms to learn important transaction patterns. |
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- Classifies each transaction cluster as "AML" or "Normal." |
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3. **Real-Time Monitoring** |
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- Integrates seamlessly with Kafka for real-time AML detection. |
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- Continuously updates and evaluates transaction networks. |
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## Intended Use |
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- Real-time fraud and AML monitoring by financial institutions. |
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- Enhanced accuracy in identifying and mitigating financial crimes. |
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## Frameworks & Technologies |
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- **PyTorch** |
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- **PyTorch Geometric** |
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- **Kafka** (for real-time integration) |
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- **NetworkX** (for graph management) |
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## Performance |
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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. |
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## How to use the model |
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Please visit the repository for detailed instructions: |
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```bash |
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git clone https://huggingface.co/Ymak7/HAMI-AML-DETECTOR |
