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

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.