| --- |
| title: Sequential Fraud Detection |
| emoji: 🛡️ |
| colorFrom: indigo |
| colorTo: gray |
| sdk: gradio |
| sdk_version: 6.22.0 |
| app_file: app.py |
| pinned: true |
| license: mit |
| short_description: A GRU scoring card transaction sequences, live |
| tags: |
| - fraud-detection |
| - pytorch |
| - deep-learning |
| - fintech |
| - gru |
| --- |
| |
| # Sequential Fraud Detection |
|
|
| Most credit card fraud models score each transaction in isolation. Fraud is a behavioural |
| signal: what matters is that *this card* has never behaved this way before. |
|
|
| This demo runs a GRU over each card's last 10 transactions and compares it against a |
| LightGBM model given the identical features without sequence context. On a held out time |
| period of 555,719 transactions the GRU reaches **0.965 PR-AUC** against **0.899**, and at the |
| cost minimising threshold it misses **31** of 2,145 frauds where LightGBM misses **112**. |
|
|
| **Every score in the first tab is a real PyTorch forward pass**, computed when you click. |
| Nothing is looked up. |
|
|
| ## The two tabs |
|
|
| **Score a transaction.** Pick a scenario, including the interesting one where the sequence |
| model catches fraud the flat model misses, and see the card's recent history alongside both |
| models' verdicts. |
|
|
| **Cost explorer.** A missed fraud costs the transaction amount; a false positive costs a |
| manual review. Move the review cost and watch the optimal threshold move with it. The |
| decision threshold is a business parameter, not a modelling constant. |
|
|
| ## Limits worth stating |
|
|
| The data is simulated (Sparkov via Kaggle). Rule generated fraud is far more learnable than |
| the adversarial kind, so 0.965 PR-AUC is not a production number; the relative comparison |
| between models is the meaningful output. The cost minimising threshold is also selected on |
| the test set, which makes the dollar figures optimistic. |
|
|
| Trained on CPU: 22,577 parameters, 4 epochs, 7.1 minutes on a laptop with no GPU. |
|
|
| [Full code and methodology on GitHub](https://github.com/adwitiyashukla/DL-based-sequential-fraud-detection) |
|
|
