Faithfulness, Stability, Agreement, and Complexity of Post-hoc Explanations for Card Fraud Alerts: A Comparative Evaluation of SHAP, LIME, Permutation Importance, and Counterfactuals

Faithfulness, Stability, Agreement, and Complexity of Post-hoc Explanations for Card Fraud Alerts: A Comparative Evaluation of SHAP, LIME, Permutation Importance, and Counterfactuals

Authors

  • Yunfei Gao Electrical Engineering, Northwestern University, IL, USA
  • Chenhui Hao Civil Engineering, University of California, CA, USA

Keywords:

post-hoc explanation, fraud detection, explanation faithfulness, explanation stability

Abstract

Fraud investigators increasingly rely on post-hoc explanations to justify why a transaction was flagged, yet the reliability of these explanations on actual alert queues is rarely measured. This paper compares three alert-level explanation approaches—SHAP, LIME, and counterfactual explanations—and uses permutation feature importance (PFI) as a retrospective global-importance reference for alerts raised by logistic regression, random forest, and XGBoost classifiers on two public card-transaction datasets: a simulated dataset with documented fraud mechanisms and a real PCA-anonymized dataset. For 200 alerts per classifier, the evaluation covers faithfulness, stability, inter-method agreement, and complexity. Exact SHAP attained the highest PGI-based faithfulness in four of the six settings and showed consistently strong within-setting faithfulness correlations, while maintaining low PGU values.A global PFI ranking matched SHAP for logistic regression but lost alert-level evidence for XGBoost, where its mechanism alignment fell to 0.442. LIME was stable under input perturbations, but its top-three overlap across seeds dropped to 0.453 on the PCA data, and counterfactual importance was the least stable, with run-to-run Jaccard between 0.212 and 0.459. Agreement between explainers was partial and classifier-dependent. The results support reporting explainer configurations and seed stability whenever explanations document fraud alerts.

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Published

2026-10-01
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