Modular Architectures for Interpretable Credit Scoring for Heterogeneous Borrower Data

Ayaz A. Sunagatullin and Mohammad Reza Bahrami ()
Additional contact information
Ayaz A. Sunagatullin: Faculty of Computer Science, Innopolis University, Innopolis 420500, Russia
Mohammad Reza Bahrami: Faculty of Computer Science, Innopolis University, Innopolis 420500, Russia

JRFM, 2025, vol. 18, issue 11, 1-25

Abstract: Modern credit scoring systems must operate under increasingly complex borrower data conditions, characterized by structural heterogeneity and regulatory demands for transparency. This study proposes a modular modeling framework that addresses both interpretability and data incompleteness in credit risk prediction. By leveraging Weight of Evidence (WoE) binning and logistic regression, we constructed domain-specific sub-models that correspond to different attribute sets and integrated them through ensemble, hierarchical, and stacking-based architectures. Using a real-world dataset from the American Express default prediction challenge, we demonstrate that these modular architectures maintain high predictive performance (test Gini > 0.90) while preserving model transparency. Comparative analysis across multiple architectural designs highlights trade-offs between generalization, computational complexity, and regulatory compliance. Our main contribution is a systematic comparison of logistic regression–based architectures that balances accuracy, robustness, and interpretability. These findings highlight the value of modular decomposition and stacking for building predictive yet interpretable credit risk models.

Keywords: credit scoring; interpretable machine learning; logistic regression; modular models; financial risk; weight of evidence; heterogeneous data (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1911-8074/18/11/615/pdf (application/pdf)
https://www.mdpi.com/1911-8074/18/11/615/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jjrfmx:v:18:y:2025:i:11:p:615-:d:1787032

Access Statistics for this article

JRFM is currently edited by Ms. Chelthy Cheng

More articles in JRFM from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().