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Fraud Detection with Graph Neural Networks

GNN-based fraud detection that models transactions as a graph (users, merchants, devices as nodes, transactions as edges). The idea is that fraudsters operate in connected networks, so graph structure should help catch patterns a flat classifier would miss.

Why GNNs for fraud?

Traditional fraud detection treats each transaction independently. But fraud often involves patterns across multiple accounts — shared devices, money laundering rings, suspicious merchant clusters. By building a graph and running message passing over it, the model can learn these relational patterns.

I implemented three architectures to compare: GraphSAGE, GAT (attention-based), and GIN. Also tried a heterogeneous version with separate node types for users/merchants/devices, which ended up working best (makes sense — different entity types should have different representations).

Results

On synthetic data (10k transactions, 5% fraud rate):

Model AUC-ROC F1
GraphSAGE 0.94 0.89
GAT 0.95 0.90
GIN 0.94 0.88
HeteroGNN 0.96 0.91

Note: Synthetic fraud signals are deliberately easy to spot (high amounts + unusual hours), so these numbers mostly reflect data separability, not real-world GNN performance. The relative ranking between architectures is more informative than the absolute numbers. To properly evaluate this you'd want a real dataset like IEEE-CIF or the Kaggle credit card dataset.

Setup

python -m venv venv
source venv/bin/activate
pip install -r requirements.txt

# Train (uses synthetic data by default)
python scripts/train.py --model graphsage --epochs 100

# Or with config file
python scripts/train.py --config configs/model_config.yaml

There's also a Makefile:

make train          # GraphSAGE
make train-gat      # GAT variant
make train-gin      # GIN
make test

Project structure

src/
├── data/           # graph construction, features, dataset
├── models/         # GraphSAGE, GAT, GIN, hetero GNN
├── training/       # training loop, focal loss
└── inference/      # predictor, GNNExplainer wrapper

Things I struggled with

  • Mini-batch training on graphs is weird. You can't just slice the data like with images — you need neighbor sampling or the subgraph structure breaks. Took me a while to get this right with PyG's utilities
  • Class imbalance + message passing = bad time. The majority class signal propagates through the graph and drowns out the fraud signal. Focal loss helped a lot here, more than I expected
  • Heterogeneous graphs are painful to debug. The forward pass kept crashing because edge types weren't matching up. The "wip: heterogeneous gnn (not working yet)" commit was real frustration
  • PyG's API changes between versions. Had to rewrite the explainer setup twice because the API changed

What I'd do differently

  • Use a real dataset (the Kaggle credit card one, or IEEE-CIF). Synthetic data is nice for development but doesn't really prove anything
  • Try temporal graph networks (TGN) — the time dimension matters a lot for fraud and I'm not capturing it well enough with just edge features
  • The inference module is pretty basic. In production you'd want incremental graph updates instead of rebuilding from scratch
  • More experiments with neighbor sampling strategies — I just used the defaults

References