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A Hybrid Approach to Fraud Detection: Combining Machine Learning and Symbolic Rules for Enhanced Performance and Explainability

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The hybrid model improves fraud detection accuracy while ensuring explainability for financial applications.
XGBoost outperformed other supervised learning models, like Logistic Regression and Random Forest, in performance metrics.
Utilizing explainable AI techniques such as SHAP and LIME enhances interpretability of the prediction results.
New feature creation based on symbolic rules effectively integrates domain knowledge with machine learning capabilities.

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Comparative analysis identifies optimal supervised learning model in vehicle claim fraud detection, highlighting enhanced explainability.

Mewada et al. (2025) studied this question.

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