File size: 1,280 Bytes
1613e73 6e27e66 1613e73 6e27e66 5a8e22b 6e27e66 5a8e22b bc918a8 1613e73 6e27e66 1613e73 6e27e66 1613e73 6e27e66 5a8e22b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 | ---
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-) |