A Bayesian belief network for corporate credit risk assessment

grantor: University of TorontoThe traditional methods used for credit risk have a number of shortcomings associated with them. Bayesian Belief Networks application for decision-making under uncertainty is widespread and the uncertainty and objectivity inherent in assessing corporate risk makes it an ideal application area. A BBN was developed to assess the credit rating and credit grade for a group of companies operating in the U.S. retail industry. The revised BBN outperformed the original BBN by correctly classifying 34% of the credit ratings and 59% of the credit grades as compared to 26% of the credit ratings and 40% of the credit grades in the original BBN. Also, the results suggest that qualitative information on a company has much influence in the assessment of credit risk. The encouraging results of using BBNs in credit risk assessment invite research in the development of better models, using larger and more representative data sets for improved analysis.M.A.Sc