A Novel Credit Model Risk Measure: does more data lead to lower model risk in credit scoring models?

Large databases and Machine Learning have increased our ability to produce models with a different number of observations and explanatory variables. The credit scoring literature has focused on the optimization of classifications. Little attention has been paid to the inadequate use of models. This study fills this gap by focusing on model risk. It proposes a measure to assess credit scoring model risk. Its emphasis is on model misuse. The proposed model risk measure is ordinal, and it applies to many settings and types of loan portfolios, allowing comparisons of different specifications and situations (as in-sample or out-of-sample data). It allows practitioners and regulators to evaluate and compare different credit risk models in terms of model risk. We empirically test our measure in plugin LASSO default models and find that adding loans from different banks to increase the number of observations is not optimal, challenging the generally accepted assumption that more data leads to better predictions.