COUNT DATA MODELS FOR A CREDIT SCORING SYSTEM
Montserrat Guillen () and
Manuel Artís ()
Risk and Insurance from University Library of Munich, Germany
Credit scoring systems created for the evaluation of new applications are based on the available statistical information which is related to the behaviour of former clients with credit. Usually, financial institutions apply discriminant analysis techniques to create these systems but they lack of good properties due, for example, to the presence of non-normal variables. As an alternative, the future repayment behaviour is predicted by means of the expected number of unpaid instalments. The use of this latter variable suggests that appropriate models might be of interest, in which some covariant exogenous variables are included in order to specify the expected level of debt. At this point, prepayment is not explicitly considered. These models should be used as explanatory tools when evaluating the level of risk involved in personal credit transactions. Negative Binomial Distribution models are suitable when heterogeneity is taken into account. Some results related to prediction performance are shown for different model specifications in the case of data from a Spanish bank.
Keywords: count data; NBD models; credit scoring. (search for similar items in EconPapers)
Note: Postscript (ASCII) RMI GUILLEN.ABS
References: View references in EconPapers View complete reference list from CitEc
Citations Track citations by RSS feed
Downloads: (external link)
Journal Article: Count data models for a credit scoring system (1996)
Working Paper: COUNT DATA MODELS FOR A CREDIT SCORING SYSTEM (1994)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:wpa:wuwpri:9407004
Access Statistics for this paper
More papers in Risk and Insurance from University Library of Munich, Germany
Bibliographic data for series maintained by EconWPA ().