SHAP-Instance Weighted and Anchor Explainable AI: Enhancing XGBoost for Financial Fraud Detection

https://doi.org/10.28991/ESJ-2024-08-06-016
Fraud Detection SHAP-Instance Weighted Anchor Explainable AI Optuna with Hyperband.

Authors

  • Putthiporn Thanathamathee 1) School of Engineering and Technology, Walailak University, Nakhon Si Thammarat 80160, Thailand. 2) Research Center for Intelligent Technology and Integration, School of Engineering and Technology, Walailak University, Nakhon Si Thammarat 80160, Thailand.
  • Siriporn Sawangarreerak
    siriporn.sa@wu.ac.th
    School of Accountancy and Finance, Walailak University, Nakhon Si Thammarat 80160,, Thailand
  • Siripinyo Chantamunee 1) School of Engineering and Technology, Walailak University, Nakhon Si Thammarat 80160, Thailand. 4) Informatics Innovation Centre of Excellence, Walailak University, Nakhon Si Thammarat 80160, Thailand.
  • Dinna Nina Mohd Nizam User Experience Research Group, Faculty of Computing and Informatics, Universiti Malaysia Sabah, W.P.Labuan,, Malaysia
Vol. 8 No. 6 (2024): December
Research Articles

Downloads

This research aims to enhance financial fraud detection by integrating SHAP-Instance Weighting and Anchor Explainable AI with XGBoost, addressing challenges of class imbalance and model interpretability. The study extends SHAP values beyond feature importance to instance weighting, assigning higher weights to more influential instances. This focuses model learning on critical samples. It combines this with Anchor Explainable AI to generate interpretable if-then rules explaining model decisions. The approach is applied to a dataset of financial statements from the listed companies on the Stock Exchange of Thailand. The method significantly improves fraud detection performance, achieving perfect recall for fraudulent instances and substantial gains in accuracy while maintaining high precision. It effectively differentiates between non-fraudulent, fraudulent, and grey area cases. The generated rules provide transparent insights into model decisions, offering nuanced guidance for risk management and compliance. This research introduces instance weighting based on SHAP values as a novel concept in financial fraud detection. By simultaneously addressing class imbalance and interpretability, the integrated approach outperforms traditional methods and sets a new standard in the field. It provides a robust, explainable solution that reduces false positives and increases trust in fraud detection models.

 

Doi: 10.28991/ESJ-2024-08-06-016

Full Text: PDF