Should We Trust the Credit Decisions Provided by Machine Learning Models?

Andrés Alonso-Robisco () and José Manuel Carbó ()
Additional contact information
Andrés Alonso-Robisco: Banco de España
José Manuel Carbó: Banco de España

Computational Economics, 2025, vol. 66, issue 5, No 23, 4245-4274

Abstract: Abstract Automated decisions provided by machine learning algorithms are rapidly gaining traction and shaping lending markets, affecting businesses’ performance and consumers’ well-being. Consequently, financial authorities are adapting the regulation, requiring that credit decisions are explainable. Although there are post hoc interpretability techniques capable of fulfilling this task, there is discussion about their reliability. In this article we propose a novel framework to test it. Our work is based on generating datasets intended to resemble typical credit settings, in which we define the importance of the variables. We then use XGBoost and Deep Learning on these datasets, and explain their predictions using SHapley Additive exPlanations (SHAP) and permutation Feature Importance. Finally, we calculate to what extent these explanations match the underlying important variables. Our results suggest that SHAP is better at capturing relevant variables, although the explanations may vary significantly depending on the characteristics of the dataset and model used.

Keywords: Synthetic data; Machine learning; Interpretability; Credit Scoring (search for similar items in EconPapers)
JEL-codes: C55 C63 G17 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10614-025-10855-x Abstract (text/html)
Access to the full text of the articles in this series is restricted.

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:kap:compec:v:66:y:2025:i:5:d:10.1007_s10614-025-10855-x

Ordering information: This journal article can be ordered from
http://www.springer. ... ry/journal/10614/PS2

DOI: 10.1007/s10614-025-10855-x

Access Statistics for this article

Computational Economics is currently edited by Hans Amman

More articles in Computational Economics from Springer, Society for Computational Economics Contact information at EDIRC.
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().