Engineering and Technology
OPEN ACCESS
Interpretable AI in Credit Scoring: A Comparative Survey of SHAP, LIME, and Hybrid Approaches
DOI
Crossref DOI
10.37547/tajet/v7i11-304
tajet
2025
VOL. 7
/ NO. 11
NOVEMBER
RESEARCH OVERVIEW
Abstract
Explainable AI (XAI) is critical in domains like credit scoring where model decisions must be transparent and accountable. This survey paper compares three local explanation techniques—SHAP, LIME, and ensemble Hybrid approach that integrates both. We evaluate these methods on consistency, variability, and suitability for regulatory environments. Emphasis is placed on use in credit risk modeling, with insights drawn from both literature and practical evaluation.
RESEARCH TOPICS
Keywords
Explainable AI (XAI)
SHAP
LIME
Local interpretability
Hybrid model explanations
Credit Risk Modeling
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