QA: What are benchmark performance indications for both application and behavioural credit scoring in a PD modelling context?

By: Bart Baesens, Seppe vanden Broucke Read and comment on this article on Medium

This QA first appeared in Data Science Briefings, the DataMiningApps newsletter as a “Free Tweet Consulting Experience” — where we answer a data science or analytics question of 140 characters maximum. Also want to submit your question? Just Tweet us @DataMiningApps. Want to remain anonymous? Then send us a direct message and we’ll keep all your details private. Subscribe now for free if you want to be the first to receive our articles and stay up to data on data science news, or follow us @DataMiningApps.


You asked: What are benchmark performance indications for both application and behavioural credit scoring in a PD modelling context?

Our answer: Application scorecards usually have an area under the ROC curve (AUC) of about 70% to 80% with about 10–15 variables (e.g. age, income, employment status, years client, etc.) on average.

Since behavioural scoring data sets have more variables, their AUC performance is typically somewhat higher ranging between 75%-90% with also about 10–15 variables (e.g. delinquency status information).