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A novel credit model risk measure: Do more data lead to lower model risk?

Author

Listed:
  • Yoshida, Valter T.
  • Schiozer, Rafael
  • de Genaro, Alan
  • dos Santos, Toni R.E.

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.

Suggested Citation

  • Yoshida, Valter T. & Schiozer, Rafael & de Genaro, Alan & dos Santos, Toni R.E., 2025. "A novel credit model risk measure: Do more data lead to lower model risk?," The Quarterly Review of Economics and Finance, Elsevier, vol. 100(C).
  • Handle: RePEc:eee:quaeco:v:100:y:2025:i:c:s1062976925000018
    DOI: 10.1016/j.qref.2025.101960
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    More about this item

    Keywords

    Model risk; Model selection; Credit risk; Credit scoring; Big data; Machine learning;
    All these keywords.

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis

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