How worth is improving predictive accuracy of credit rating models?

• The paper quantifies the economic value of predictive accuracy in credit rating models • It measures profit gains associated with increase in the Area Under the Curve statistic • The results indicate that a 1% rise in AUROC translates on average into a 1.1% increase in bank profits, This study addresses an underexplored question in the credit risk literature: How valuable is improving credit rating model accuracy? The motivation is twofold. First, quantifying this value helps bank executives assess returns on investment from enhancing rating models, including adopting advanced machine learning (ML) and artificial intelligence (AI) techniques. Second, it enables supervisors and validators to evaluate the economic significance of model performance declines. Using simulated data and conservative assumptions, we estimate additional profits from increases in discriminatory power, measured by the Area Under the Receiver Operating Characteristic curve (AUROC). Our results show that a 1% AUROC increase leads to a 1.1% bank profit increase in our baseline scenario, highlighting substantial economic benefits of higher model accuracy. These findings underscore the importance of investing in richer data sources and sophisticated ML/AI approaches.