Update README.md

README.md

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----
-license: mit
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+---
+license: mit
+datasets:
+- Ymak7/transactional-data
+language:
+- en
+---
+
+# 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:
+```bash
+git clone https://huggingface.co/Ymak7/HAMI-AML-DETECTOR