Fraud Detection Pipeline
by Community · open-source · Last verified 2026-03-17
End-to-end fraud detection pipeline combining XGBoost/LightGBM with isolation forest anomaly detection, handling severe class imbalance via SMOTE-Tomek resampling and cost-sensitive learning. Includes a real-time scoring API with sub-10ms latency, feature drift monitoring, and an explainability layer for dispute resolution.
https://github.com/scikit-learn-contrib/imbalanced-learn ↗B
B—Above Average
Adoption: B+Quality: AFreshness: ACitations: BEngagement: F
Specifications
- License
- MIT
- Pricing
- open-source
- Capabilities
- real-time-scoring, anomaly-detection, smote-resampling, shap-explanations
- Integrations
- xgboost, lightgbm, imbalanced-learn, shap, fastapi, redis
- Use Cases
- payment-fraud, account-takeover, insurance-claims-fraud
- API Available
- Yes
- Language
- python
- Dependencies
- xgboost, lightgbm, imbalanced-learn, shap, fastapi, redis, pandas
- Environment
- Python 3.10+
- Est. Runtime
- Training: 10-30 min; inference: <10ms
- Tags
- fraud-detection, anomaly-detection, imbalanced-learning, xgboost, real-time
- Added
- 2026-03-17
- Completeness
- 100%
Index Score
63.7Adoption
75
Quality
87
Freshness
85
Citations
65
Engagement
0