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Arxiv
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178
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Financial Fraud Detection Using Explainable AI and Stacking Ensemble Methods
- Researchers propose a fraud detection framework using a stacking ensemble of XGBoost, LightGBM, and CatBoost models.
- Explainable artificial intelligence (XAI) techniques like SHAP, LIME, PDP, and PFI are employed to enhance model transparency and interpretability.
- The model achieved high performance metrics with 99% accuracy and an AUC-ROC score of 0.99 on the IEEE-CIS Fraud Detection dataset.
- Combining high prediction accuracy with transparent interpretability could lead to a more ethical and trustworthy solution in financial fraud detection.
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