GraphShield

GraphShield is an AI-powered Anti-Money Laundering (AML) system that combines Graph Neural Networks, traditional Machine Learning, and Explainable AI to detect suspicious transaction patterns and provide investigators with transparent reasoning behind every prediction.

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Problem

Traditional AML systems mainly rely on rule-based detection.

Although effective for known patterns, they struggle with:

• High false positive rates
• Complex fraud networks
• Hidden relationships between transactions
• Lack of explanation behind alerts

This creates unnecessary investigation workload and makes decision-making difficult.

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Solution

GraphShield models financial transactions as a graph.

Each transaction becomes a node, and relationships between transactions become edges.

The system learns both:

1. Transaction-level behavior
2. Network-level relationships

to identify suspicious activity.

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Features

Hybrid Detection

Combines:

• Graph Attention Networks (GATv2)
• Gradient Boosted Decision Trees (XGBoost)

to improve fraud detection reliability.

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Graph-Based Analysis

Represents transactions as connected networks to identify:

• Suspicious transaction clusters
• Hidden relationships
• High-risk neighbors

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Explainable AI

Provides:

• Feature contribution analysis using SHAP
• Important graph connections using GNNExplainer
• Risk reasoning for flagged transactions

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Interactive Visualization

Uses 3D graph visualization to allow investigators to:

• Explore transaction networks
• Identify suspicious nodes
• Inspect relationships

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Technologies

• Python
• PyTorch
• PyTorch Geometric
• Scikit-learn
• XGBoost
• SHAP
• NetworkX
• ForceGraph3D

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Dataset

This project uses:

Elliptic Bitcoin Transaction Dataset

A public benchmark dataset containing:

• 200K+ transactions
• Transaction relationships
• Engineered transaction features
• Labels: licit, illicit, unknown

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Evaluation

The system is evaluated using:

• ROC-AUC
• Precision
• Recall
• F1-score
• Confusion Matrix

Special focus is placed on:

• Illicit transaction detection
• Avoiding data leakage
• Preventing overfitting

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Team

GraphShield team

Architecture

Architecture

GraphShield/
│
├── README.md
├── requirements.txt
├── .gitignore
├── .gitattributes
│
├── app/                              # Frontend / Streamlit application
│   │
│   ├── main.py                       # Streamlit entry point
│   │
│   ├── pages/
│   │    ├── dashboard.py             # Overall AML dashboard + 3D graph
│   │    ├── transaction_analysis.py  # Risk + explanation view
│   │    └── network_view.py          # Predictions table + metrics
│   │
│   ├── components/
│   │    ├── __init__.py
│   │    ├── data_loader.py           # Cached artifact loader (all paths)
│   │    ├── graph_builder.py         # Builds node/edge data for 3D viewer
│   │    ├── graph_viewer.py          # ForceGraph3D HTML component
│   │    ├── risk_card.py             # Risk score display widget
│   │    ├── explanation_panel.py     # SHAP + GNN explanation display
│   │    └── charts.py               # Metrics / plots
│   │
│   └── assets/
│
│
├── backend/                          # AI backend logic
│   │
│   ├── models/                       # Model definitions only
│   │    ├── gatv2.py
│   │    ├── xgboost_model.py
│   │    └── hybrid_model.py
│   │
│   ├── inference/                    # Loading weights + prediction
│   │    ├── predictor.py
│   │    └── risk_scoring.py
│   │
│   ├── explainability/               # Reading / generating explanations
│   │    ├── shap_explainer.py
│   │    └── gnn_explainer.py
│   │
│   └── visualization/
│        └── force_graph.py
│
│
├── data/
│   └── README.md
│
│
├── results/
│   │
│   ├── predictions/
│   │    ├── hybrid_predictions.csv
│   │    ├── xgb_predictions.csv
│   │    └── gatv2_predictions.csv
│   │
│   ├── explanations/
│   │    ├── shap/
│   │    │    └── transaction_explanations.csv
│   │    └── gnn/
│   │         ├── important_nodes.csv
│   │         ├── important_edges.csv
│   │         └── explanation_graph.json
│   │
│   ├── graphs/
│   │    ├── pyg_graph.pt
│   │    └── fraud_network.json
│   │
│   ├── embeddings/
│   │    └── transaction_ids.csv
│   │
│   ├── shared/
│   │    └── feature_categories.json
│   │
│   └── metrics/
│        ├── final_metrics.json
│        ├── confusion_matrices.png
│        └── roc_curve.png
│
│
├── notebooks/
│   ├── 01_training.ipynb
│   ├── 02_explainability.ipynb
│   └── 03_visualization.ipynb
│
│
└── deployment/
    ├── Dockerfile
    └── azure_deployment.md

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Running the App

pip install -r requirements.txt
streamlit run app/main.py