A Bayesian belief network for corporate credit risk assessment

by Pershad, Rinku.

Abstract (Summary)
The traditional methods used for creâit risk have a number of shortcomings associated with them. Bayesian Beiief Networks application for decision-making under uncertainty is widespread and the uncatainty and objectivity inherent in assessing corporate risk makes it an ideal application area. A BBN was developed to assess the credit rating and crecüt grade for a group of companies opemting in the US. retail industry. The revised BBN outperformed the original BBN by comctly classifying 34% of the credit ratings and 59%of the credit grades as compared to 26%of the =dit 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 d t risic. The encouragingnsults of using BBNs in credit nsk assessment invite research in the development of better models. using larger and more representative data sets for improved anal ysis.
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Source Type:Master's Thesis



Date of Publication:01/01/2000

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