Enhanced decision support in credit scoring using Bayesian binary quantile regression
V L Miguéis,
D F Benoit and
Dirk Van den Poel ()
Additional contact information
V L Miguéis: University of Porto, Porto, Portugal
D F Benoit: Ghent University, Gent, Belgium
Journal of the Operational Research Society, 2013, vol. 64, issue 9, 1374-1383
Fierce competition as well as the recent financial crisis in financial and banking industries made credit scoring gain importance. An accurate estimation of credit risk helps organizations to decide whether or not to grant credit to potential customers. Many classification methods have been suggested to handle this problem in the literature. This paper proposes a model for evaluating credit risk based on binary quantile regression, using Bayesian estimation. This paper points out the distinct advantages of the latter approach: that is (i) the method provides accurate predictions of which customers may default in the future, (ii) the approach provides detailed insight into the effects of the explanatory variables on the probability of default, and (iii) the methodology is ideally suited to build a segmentation scheme of the customers in terms of risk of default and the corresponding uncertainty about the prediction. An often studied dataset from a German bank is used to show the applicability of the method proposed. The results demonstrate that the methodology can be an important tool for credit companies that want to take the credit risk of their customer fully into account.
References: Add references at CitEc
Citations: View citations in EconPapers (4) Track citations by RSS feed
Downloads: (external link)
http://www.palgrave-journals.com/jors/journal/v64/n9/pdf/jors2012116a.pdf Link to full text PDF (application/pdf)
http://www.palgrave-journals.com/jors/journal/v64/n9/full/jors2012116a.html Link to full text HTML (text/html)
Access to full text is restricted to subscribers.
Working Paper: Enhanced Decision Support in Credit Scoring Using Bayesian Binary Quantile Regression (2012)
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:pal:jorsoc:v:64:y:2013:i:9:p:1374-1383
Ordering information: This journal article can be ordered from
http://www.springer. ... search/journal/41274
Access Statistics for this article
Journal of the Operational Research Society is currently edited by Tom Archibald and Jonathan Crook
More articles in Journal of the Operational Research Society from Palgrave Macmillan, The OR Society
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().