Graph Neural Network Fraud Detection
Overview
A Graph Convolutional Network (GCN) for fraud detection trained on a public dataset (80/20 split). The model was attacked with adversarial samples to test robustness, then fine-tuned on those samples to harden it against future attacks — improving overall accuracy in the process. Outcome: Produced a fraud detection model that is both more accurate and noticeably more robust to adversarial input.
Architecture & Pipeline
flowchart LR
n0["Transaction GraphPublic dataset"]
n1["Train/Test Split80 / 20"]
n2["GCN TrainingPyTorch"]
n3["Adversarial AttackRobustness probe"]
n4["Adversarial Fine-tuneHardened model"]
n5["Production ClassifierHigher accuracy + robustness"]
n0 --> n1
n1 --> n2
n2 --> n3
n3 --> n4
n4 --> n5
classDef step0 fill:#f1f5f9,stroke:#64748b,color:#1e293b,stroke-width:2px,rx:10,ry:10;
classDef step1 fill:#ecfeff,stroke:#06b6d4,color:#1e293b,stroke-width:2px,rx:10,ry:10;
classDef step2 fill:#f0fdfa,stroke:#0d9488,color:#1e293b,stroke-width:2px,rx:10,ry:10;
classDef step3 fill:#ecfdf5,stroke:#10b981,color:#1e293b,stroke-width:2px,rx:10,ry:10;
classDef step4 fill:#fffbeb,stroke:#f59e0b,color:#1e293b,stroke-width:2px,rx:10,ry:10;
class n0 step0;
class n1 step1;
class n2 step2;
class n3 step2;
class n4 step3;
class n5 step4;
End-to-end flow derived from this project's scope and tech stack. Tap View Fullscreen for a larger view, or scroll horizontally on small screens.
Key Features
- GCN architecture for graph-structured fraud data
- Adversarial attack evaluation
- Adversarial fine-tuning for hardened robustness
- Tech Stack:** Python, PyTorch