A Bayesian Analysis of a Random Effects Small Business Loan Credit Scoring Model

Patrick J. Farrell, Brenda MacGibbon, Thomas J. Tomberlin, Dale Doreen

Abstract


One of the most important aspects of credit scoring is constructing a model that has low misclassification rates and is also flexible enough to allow for random variation. It is also well known that, when there are a large number of highly correlated variables as is typical in studies involving questionnaire data, a method must be found to reduce the number of variables to those that have high predictive power. Here we propose a Bayesian multivariate logistic regression model with both fixed and random effects for small business loan credit scoring and a variable reduction method using Bayes factors. The method is illustrated on an interesting data set based on questionnaires sent to loan officers in Canadian banks and venture capital companies

Keywords


Bayes Factors, Credit Scoring, MCMC, Variable Selection

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DOI: http://dx.doi.org/10.18187/pjsor.v7i2-Sp.307

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Title

A Bayesian Analysis of a Random Effects Small Business Loan Credit Scoring Model

Keywords

Bayes Factors, Credit Scoring, MCMC, Variable Selection

Description

One of the most important aspects of credit scoring is constructing a model that has low misclassification rates and is also flexible enough to allow for random variation. It is also well known that, when there are a large number of highly correlated variables as is typical in studies involving questionnaire data, a method must be found to reduce the number of variables to those that have high predictive power. Here we propose a Bayesian multivariate logistic regression model with both fixed and random effects for small business loan credit scoring and a variable reduction method using Bayes factors. The method is illustrated on an interesting data set based on questionnaires sent to loan officers in Canadian banks and venture capital companies

Date

2011-09-29

Identifier


Source

Pakistan Journal of Statistics and Operation Research; Vol 7. No. 2-Sp, Oct 2011



Print ISSN: 1816-2711 | Electronic ISSN: 2220-5810