Vol. 4 No. 2 (2024): July−December 2024
Articles

Comparative Analysis of Interpretability Techniques for Feature Importance in Credit Risk Assessment

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  • Zhuxuanzi Wang,
  • Jiayu Liang
Zhuxuanzi Wang
Information Systems, Cornell Tech, NY, USA
Jiayu Liang
Applied Statistics, Cornell University, NY, USA

Published 2024-11-14

Keywords

  • Explainable AI,
  • Credit Risk Assessment,
  • Feature Importance,
  • Model Interpretability

How to Cite

Wang, Z., & Liang, J. (2024). Comparative Analysis of Interpretability Techniques for Feature Importance in Credit Risk Assessment. Spectrum of Research, 4(2). Retrieved from https://spectrumofresearch.com/index.php/sr/article/view/21

Copyright (c) 2024 Spectrum of Research

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

This study presents a comprehensive comparative analysis of interpretability techniques for feature importance in credit risk assessment models. As financial institutions increasingly adopt complex machine learning algorithms for credit decisioning, the need for transparent and interpretable models has become critical for regulatory compliance and stakeholder trust. We evaluated multiple explainability approaches including SHAP, LIME, and model-specific techniques across diverse credit risk models using standardized datasets containing 58,645 loan applications. Performance assessment utilized a multi-dimensional framework encompassing explanation quality, computational efficiency, and implementation complexity. Results demonstrate that TreeSHAP achieves the highest aggregate performance (0.88) by balancing strong explanation fidelity with reasonable computational requirements, while LIME exhibits advantages in efficiency and user comprehension. The analysis reveals consistent identification of key risk factors across techniques, with loan interest rate, employment length, and loan-to-income ratio universally recognized as critical predictors. Our comparative framework provides financial institutions with actionable guidance for technique selection based on specific operational contexts, while establishing quantitative benchmarks for regulatory compliance. The findings contribute to the advancement of responsible AI deployment in financial services by enhancing model transparency without sacrificing predictive performance.

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