Explainable AI in Financial Fraud and Anomaly Detection

This repository implements a hybrid, explainable fraud/anomaly detection pipeline that combines:

• Graph Neural Networks (GNNs) over a transaction graph
• Tabular Gradient Boosting (baseline) for calibrated probabilities
• Autoencoder for unsupervised anomaly scoring
• Post‑hoc explainability via SHAP, Permutation Importance, and GNNExplainer-like node/edge importance
• Drift tracking (population stability index, PSI) and threshold optimization (F1/PR-AUC maximizing)

It includes a configurable training CLI, reproducible experiments, and a lightweight unit test suite.

Quickstart

pip install -r requirements.txt
python -m src.experiments.main --config configs/default_config.json
# visualize
python -m src.experiments.evaluate --artifacts results/run_000

Project Structure

explainable-ai-fraud-detection/
├── README.md
├── requirements.txt
├── configs/
│   └── default_config.json
├── data/
│   └── transactions.csv                # sample synthetic data
├── src/
│   ├── data/
│   │   ├── build_graph.py
│   │   └── tabular_features.py
│   ├── models/
│   │   ├── gnn_model.py
│   │   ├── autoencoder.py
│   │   └── tabular_baseline.py
│   ├── explainability/
│   │   ├── shap_explainer.py
│   │   └── gnn_explainer_like.py
│   ├── experiments/
│   │   ├── main.py
│   │   └── evaluate.py
│   ├── utils.py
│   └── metrics.py
├── docs/
│   └── project_report.md
├── results/
│   └── (artifacts saved here)
└── tests/
    └── test_shapes.py

Reproducible Runs

• All hyperparameters and seeds are in configs/default_config.json.
• Artifacts (metrics, confusion matrix, feature importances, explainability plots) are saved under results/run_xxx.

Notes

• torch-geometric installation depends on your local CUDA/CPU stack. See https://pytorch-geometric.readthedocs.io for wheels.
• If torch-geometric is unavailable, you can run tabular + autoencoder only by setting "use_gnn": false in the config.