 A novel credit model risk measure: Do more data lead to lower model risk?Valter T. Yoshida, Rafael Schiozer, Alan de Genaro and Toni R.E. dos Santos The Quarterly Review of Economics and Finance, 2025, vol. 100, issue C Abstract: Large databases and Machine Learning enhance our capacity to develop models with many observations and explanatory variables. While the literature has primarily focused on optimizing classifications, little attention has been given to model risk, especially originating from inadequate use. To address this gap, we introduce a new metric for assessing model risk in credit applications. We test the metric using cross-section LASSO default models, each incorporating 200 thousand loan observations from several banks and more than 100 explanatory variables. The results indicate that models that use loans from a single bank have lower model risk than models using loans from the entire financial system. Therefore, adding loans from different banks to increase the number of observations in a model is suboptimal, challenging the widely accepted assumption that more data leads to better predictions. Keywords: Model risk; Model selection; Credit risk; Credit scoring; Big data; Machine learning (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:quaeco:v:100:y:2025:i:c:s1062976925000018 DOI: 10.1016/j.qref.2025.101960 Access Statistics for this article The Quarterly Review of Economics and Finance is currently edited by R. J. Arnould and J. E. Finnerty More articles in The Quarterly Review of Economics and Finance from Elsevier |
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