---
title: Hybrid AI Fraud Detection XAI Dashboard
emoji: "🛡️"
colorFrom: blue
colorTo: green
sdk: streamlit
sdk_version: 1.28.0
app_file: app.py
pinned: true
license: mit
tags:
  - fraud-detection
  - explainable-ai
  - lightgbm
  - lstm
  - graph-neural-network
  - shap
  - ieee-cis
---

# Hybrid AI Architectures for Proactive Fraud Detection
## An XAI-Driven Optimization Framework

**Auteur:** Latif SINARE
**Programme:** Master MAII - FSTH, Universite Abdelmalek Essaadi

## Objectifs quantitatifs
Recall >= 90% - Precision >= 10% - AUPRC > 0,85 - FPR < 0,01%

## Pipelines
| Phase | Dataset | Modeles | Optimiseur |
|-------|---------|---------|------------|
| 1 | Kaggle CC | LightGBM | CMA-ES |
| 2 | PaySim | LSTM + RGAT + Stacking | PSO Async |
| 3 | IEEE-CIS | LSTM + RGAT + Stacking | PSO Async |
| 4 | AMLSim | RGAT | - |

## Pages du dashboard
1. Pipeline 1 - Kaggle CC: analyse interactive de transaction + SHAP
2. Pipeline 2 - PaySim: resultats Stacking + analyse heuristique
3. Pipeline 3 - IEEE-CIS: resultats complets + PSI drift monitoring
4. Performances: comparaison des 3 phases

## References
- Lundberg and Lee (2017) - SHAP
- Lopez-Rojas et al. (2016) - PaySim
- Wang et al. (2019) - GNN Fraud Detection