Article: Better credit decisioning through scorecard surrogate models for machine learning algorithms Journal: International Journal of Data Analysis Techniques and Strategies (IJDATS) 2026 Vol.18 No.2 pp.107 - 132 Abstract: Over the last several years, the application of machine-learning models, called black-box models, has become a popular research topic in credit scoring. This study illustrates how surrogate models can be used to interpret credit decisions made using black-box models. A framework for using surrogate models in a credit scoring context is used to explain and interpret well-known machine learning models (e.g., neural networks, forests, gradient boosting, and support vector machines). This study uses real-world anonymised consumer bureau data obtained from Equifax to illustrate the degree of interpretability that can be achieved using machine learning models to assess the creditworthiness of loan applicants. The main objective of this study is to show practitioners how surrogate scorecard models can be used to interpret some of the most popular machine learning models in a credit scoring decision making process. Inderscience Publishers - linking academia, business and industry through research

Title: Better credit decisioning through scorecard surrogate models for machine learning algorithms

Authors: Billie Anderson; Naeem Siddiqi; Mark A. Newman; J. Michael Hardin

Addresses: Department of Management Science and Information Systems, School of Business, Southern New Hampshire University, Hooksett, NH 03106, USA ' SAS Canada Inc., Toronto, M5J 2T3, Canada ' Devis, 2107 Wilson Boulevard, Suite 530 Arlington, VA 22201, USA ' Department of Quantitative Analysis, Brock School of Business, Samford University, Birmingham, AL 35229, USA; Department of Biostatistics, School of Public Health, Birmingham, AL 35229, USA; Academic Affairs, Provost, Emeritus, Birmingham, AL 35229 USA

Abstract: Over the last several years, the application of machine-learning models, called black-box models, has become a popular research topic in credit scoring. This study illustrates how surrogate models can be used to interpret credit decisions made using black-box models. A framework for using surrogate models in a credit scoring context is used to explain and interpret well-known machine learning models (e.g., neural networks, forests, gradient boosting, and support vector machines). This study uses real-world anonymised consumer bureau data obtained from Equifax to illustrate the degree of interpretability that can be achieved using machine learning models to assess the creditworthiness of loan applicants. The main objective of this study is to show practitioners how surrogate scorecard models can be used to interpret some of the most popular machine learning models in a credit scoring decision making process.

Keywords: credit scoring; explainable machine learning models; surrogate models; machine learning; credit decisioning; credit risk.

DOI: 10.1504/IJDATS.2026.154787

International Journal of Data Analysis Techniques and Strategies, 2026 Vol.18 No.2, pp.107 - 132

Received: 12 Mar 2024
Accepted: 17 Aug 2024
Published online: 14 Jul 2026 *

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Title: Better credit decisioning through scorecard surrogate models for machine learning algorithms

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