An econometric analysis of the bank credit scoring problem

William J. Boyes; Dennis Hoffman; Stuart A. Low

doi:10.1016/0304-4076(89)90026-2

An econometric analysis of the bank credit scoring problem

William J. Boyes, Dennis Hoffman, Stuart A. Low

Economics

Research output: Contribution to journal › Article › peer-review

173 Scopus citations

Abstract

Most credit assessment models used in practice are based on simple credit scoring functions estimated by discriminant analysis. These functions are designed to distinguish whether or not applicants belong to the population of 'would be' defaulters. We suggest that the traditional view that emphasizes default probability is too narrow. Our model of credit assessment focuses on expected earnings. We demonstrate how maximum likelihood estimates of default probabilities can be obtained from a bivariate 'censored probit' framework using a 'choice-based' sample originally intended for discriminant analysis. The paper concludes with recommendations for combining these default probability estimates with other parameters of the loan earnings process to obtain a more meaningful model of credit assessment.

Original language	English (US)
Pages (from-to)	3-14
Number of pages	12
Journal	Journal of Econometrics
Volume	40
Issue number	1
DOIs	https://doi.org/10.1016/0304-4076(89)90026-2
State	Published - Jan 1989

ASJC Scopus subject areas

Economics and Econometrics

Access to Document

10.1016/0304-4076(89)90026-2

Cite this

@article{ec345364ca1e4f26b29195fd2cc58de6,

title = "An econometric analysis of the bank credit scoring problem",

abstract = "Most credit assessment models used in practice are based on simple credit scoring functions estimated by discriminant analysis. These functions are designed to distinguish whether or not applicants belong to the population of 'would be' defaulters. We suggest that the traditional view that emphasizes default probability is too narrow. Our model of credit assessment focuses on expected earnings. We demonstrate how maximum likelihood estimates of default probabilities can be obtained from a bivariate 'censored probit' framework using a 'choice-based' sample originally intended for discriminant analysis. The paper concludes with recommendations for combining these default probability estimates with other parameters of the loan earnings process to obtain a more meaningful model of credit assessment.",

author = "Boyes, {William J.} and Dennis Hoffman and Low, {Stuart A.}",

note = "Funding Information: *This paper was presented at the {\textquoteleft}Issues in Econometric Forecasting Conference{\textquoteright} at Arizona State Universitv in March 1987. We benefited from discussions with Marie Connollv. Mike Ormiston and Peter Schmidt. Support from the Center for Financial System Research at &iaona State University is gratefully acknowledged.",

year = "1989",

month = jan,

doi = "10.1016/0304-4076(89)90026-2",

language = "English (US)",

volume = "40",

pages = "3--14",

journal = "Journal of Econometrics",

issn = "0304-4076",

publisher = "Elsevier BV",

number = "1",

}

TY - JOUR

T1 - An econometric analysis of the bank credit scoring problem

AU - Boyes, William J.

AU - Hoffman, Dennis

AU - Low, Stuart A.

N1 - Funding Information: *This paper was presented at the ‘Issues in Econometric Forecasting Conference’ at Arizona State Universitv in March 1987. We benefited from discussions with Marie Connollv. Mike Ormiston and Peter Schmidt. Support from the Center for Financial System Research at &iaona State University is gratefully acknowledged.

PY - 1989/1

Y1 - 1989/1

N2 - Most credit assessment models used in practice are based on simple credit scoring functions estimated by discriminant analysis. These functions are designed to distinguish whether or not applicants belong to the population of 'would be' defaulters. We suggest that the traditional view that emphasizes default probability is too narrow. Our model of credit assessment focuses on expected earnings. We demonstrate how maximum likelihood estimates of default probabilities can be obtained from a bivariate 'censored probit' framework using a 'choice-based' sample originally intended for discriminant analysis. The paper concludes with recommendations for combining these default probability estimates with other parameters of the loan earnings process to obtain a more meaningful model of credit assessment.

AB - Most credit assessment models used in practice are based on simple credit scoring functions estimated by discriminant analysis. These functions are designed to distinguish whether or not applicants belong to the population of 'would be' defaulters. We suggest that the traditional view that emphasizes default probability is too narrow. Our model of credit assessment focuses on expected earnings. We demonstrate how maximum likelihood estimates of default probabilities can be obtained from a bivariate 'censored probit' framework using a 'choice-based' sample originally intended for discriminant analysis. The paper concludes with recommendations for combining these default probability estimates with other parameters of the loan earnings process to obtain a more meaningful model of credit assessment.

UR - http://www.scopus.com/inward/record.url?scp=13044262094&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=13044262094&partnerID=8YFLogxK

U2 - 10.1016/0304-4076(89)90026-2

DO - 10.1016/0304-4076(89)90026-2

M3 - Article

AN - SCOPUS:13044262094

SN - 0304-4076

VL - 40

SP - 3

EP - 14

JO - Journal of Econometrics

JF - Journal of Econometrics

IS - 1

ER -

An econometric analysis of the bank credit scoring problem

Abstract

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this