Economics & Sociology

ISSN: 2071-789X eISSN: 2306-3459 DOI: 10.14254/2071-789X
Index PUBMS: f5512f57-a601-11e7-8f0e-080027f4daa0
Article information
Title: Impact of baseline population on credit score’s predictive power
Issue: Vol. 12, No 1, 2019
Published date: 03-2019 (print) / 03-2019 (online)
Journal: Economics & Sociology
ISSN: 2071-789X, eISSN: 2306-3459
Authors: Kamphol Panyagometh
NIDA Business School, Bangkok, Thailand
Keywords: credit scoring, predictive power, logit model
DOI: 10.14254/2071-789X.2019/12-1/15
Index PUBMS: b68a6855-5d5c-11e9-8b68-fa163e6feac6
Language: English
Pages: 262-269 (8)
JEL classification: C40, C50, G30
The authors are thankful to the National Credit Bureau of Thailand (NCB) for useful comments and data support to carry out this research. The authors also would like to thank Vorayuth Pakachaipong for his contributions as a research assistant.

Credit scoring involves statistical analysis performed by lenders and financial institutions to access person's creditworthiness. It utilizes statistical techniques along with debtor data such as loan application or credit bureau information to measure debtor’s creditworthiness. When compared with the traditional credit evaluation process, credit scoring has shown less bias, faster speed, and consistent measurement of creditworthiness. For this reason, the National Credit Bureau of Thailand (NCB) has developed the NCB Score, based on credit behavior information collected from its members’ financial institutions, which normally issue a wide variety of credit products. However, problems can arise when this NCB score is applied to a smaller bank that usually offers a few specific types of loans. As a result, the score’s predictive power may deteriorate. In this paper, the impact of baseline population difference on the predictive power of a credit score was studied by separating proprietary data from NCB into two groups. One group represents those who originate personal loans in the seventh month of the study period, and the other group represents those who originate mortgage loans in the seventh month of the study period. The credit score model of each group was developed and their predictive power was compared when used with the same baseline population and a different baseline population to monitor the change in model predictive power


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