Credit rating models: merging quantitative variables and qualitative information

To estimate the probability of default of companies and the correlated rating classes, it is necessary to use efficiently the information contained in different databases. In this respect, we propose a novel approach, based on the recursive usage of Bayes theorem, that can be very helpful in integrating default estimates obtained from different sets of covariates.The application of our proposal to an Italian credit risk database shows that it performs quite efficiently, allowing to predict for each company the probability of default by averaging the covariates contribute