| --- |
| title: Fraud Detection Multi-Model |
| emoji: 🛡️ |
| colorFrom: red |
| colorTo: blue |
| sdk: streamlit |
| sdk_version: 1.57.0 |
| app_file: app.py |
| pinned: true |
| suggested_hardware: cpu-basic |
| python_version: 3.11 |
| --- |
| |
| # Fraud Detection — Multi-Model Live Monitor |
|
|
| Real-time transaction fraud detection using a 3-layer ensemble pipeline. |
|
|
| ## Architecture |
|
|
| ``` |
| Transaction |
| │ |
| ▼ |
| Layer 1 — Hard Rules |
| │ Rule 1: Accessibility + Remote App → Block immediately |
| │ Rule 2: Hesitation > 45s + New recipient → bias score ≥ 0.6 |
| │ |
| ▼ |
| Layer 2 — Ensemble Scoring |
| │ Model A (Sender Behavioral): LightGBM / XGBoost |
| │ Model B (Payee Risk Profile): LightGBM |
| │ final_score = 0.3 × prob_a + 0.7 × prob_b |
| │ |
| ▼ |
| Layer 3 — Threshold Decision |
| score ≥ 0.80 → Block |
| score ≥ 0.50 → Alert + OTP |
| score < 0.50 → Pass |
| ``` |
|
|
| ## Files required |
|
|
| | File | Purpose | |
| |------|---------| |
| | `model_a_pipeline.joblib` | Sender behavioral model | |
| | `model_b_pipeline.joblib` | Payee risk profile model | |
| | `config.json` | Feature list, weights, thresholds | |
| | `payee_profile_store.csv` | Historical payee lookup table | |
|
|
| ## GitHub |
|
|
| [Fraud-Detection-Multi-Model](https://github.com/PlamGG/Fraud-Detection-Multi-Model-) |
