Credit Card Fraud Detection
Real-time fraud detection using XGBoost and SHAP achieving 97% ROC-AUC and $131K savings per 100K transactions
Technology Stack
ROI
5,000%+
Savings
$131K per 100K transactions
Accuracy
97% ROC-AUC
Key Performance Indicators
Business Impact Summary
| Return on Investment | 5,000%+ |
| Cost Savings | $131K per 100K transactions |
| Model Accuracy | 97% ROC-AUC |
| Fraud Detection Rate | 91% |
| False Positive Rate | 0.6% |
| Latency | <100ms |
Overview
Production ML system detecting credit card fraud with 97% ROC-AUC, 91% recall, and <100ms latency. SHAP explanations provide transparency for regulatory compliance. Saves $131K per 100K transactions while minimizing customer friction.
Live Demo
Live Demo
Interactive Streamlit application
Credit Card Fraud Detection Demo
Click below to load the interactive demo
This is a live, interactive demo deployed on Streamlit Cloud. You can:
- •Upload your own data for predictions
- •Explore model performance metrics
- •View interactive visualizations
- •Understand model predictions with explainability
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