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Updating a credit-scoring model based on new attributes without realization of actual data

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  • Ju, Yong Han
  • Sohn, So Young

Abstract

Funding small and medium-sized enterprises (SMEs) to support technological innovation is critical for national competitiveness. Technology credit scoring models are required for the selection of appropriate funding beneficiaries. Typically, a technology credit-scoring model consists of several attributes and new models must be derived every time these attributes are updated. However, it is not feasible to develop new models until sufficient historical evaluation data based on these new attributes will have accumulated. In order to resolve this limitation, we suggest the framework to update the technology credit scoring model. This framework consists of ways to construct new technology credit-scoring model by comparing alternative scenarios for various relationships between existing and new attributes based on explanatory factor analysis, analysis of variance, and logistic regression. Our approach can contribute to find the optimal scenario for updating a scoring model.

Suggested Citation

  • Ju, Yong Han & Sohn, So Young, 2014. "Updating a credit-scoring model based on new attributes without realization of actual data," European Journal of Operational Research, Elsevier, vol. 234(1), pages 119-126.
    • Handle: RePEc:eee:ejores:v:234:y:2014:i:1:p:119-126
    DOI: 10.1016/j.ejor.2013.02.030
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    References listed on IDEAS

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    1. Sohn, So Young & Kim, Hong Sik, 2007. "Random effects logistic regression model for default prediction of technology credit guarantee fund," European Journal of Operational Research, Elsevier, vol. 183(1), pages 472-478, November.
    2. Thomas A. Flint, 1997. "Predicting Student Loan Defaults," The Journal of Higher Education, Taylor & Francis Journals, vol. 68(3), pages 322-354, May.
    3. T H Moon & S Y Sohn, 2010. "Technology credit scoring model considering both SME characteristics and economic conditions: The Korean case," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(4), pages 666-675, April.
    4. Evžen Kocenda & Martin Vojtek, 2011. "Default Predictors in Retail Credit Scoring: Evidence from Czech Banking Data," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 47(6), pages 80-98, November.
    5. Ale Ebrahim, Nader & Ahmed, Shamsuddin & Abdul Rashid, Salwa Hanim & Taha, Zahari, 2010. "The Effectiveness of Virtual R&D Teams in SMEs: Experiences of Malaysian SMEs," MPRA Paper 27368, University Library of Munich, Germany, revised Nov 2010.
    6. S Y Sohn & M K Doo & Y H Ju, 2012. "Pattern recognition for evaluator errors in a credit scoring model for technology-based SMEs," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 63(8), pages 1051-1064, August.
    7. Paleologo, Giuseppe & Elisseeff, André & Antonini, Gianluca, 2010. "Subagging for credit scoring models," European Journal of Operational Research, Elsevier, vol. 201(2), pages 490-499, March.
    8. Amini, Mehdi & Wakolbinger, Tina & Racer, Michael & Nejad, Mohammad G., 2012. "Alternative supply chain production–sales policies for new product diffusion: An agent-based modeling and simulation approach," European Journal of Operational Research, Elsevier, vol. 216(2), pages 301-311.
    9. Inha Oh & Jeong-Dong Lee & Almas Heshmati & Gyoung-Gyu Choi, 2009. "Evaluation of credit guarantee policy using propensity score matching," Small Business Economics, Springer, vol. 33(3), pages 335-351, October.
    10. Gopinath, C., 1995. "Bank strategies toward firms in decline," Journal of Business Venturing, Elsevier, vol. 10(1), pages 75-92, January.
    11. Ilhyock Shim, 2006. "Corporate credit guarantees in Asia," BIS Quarterly Review, Bank for International Settlements, December.
    12. Stiglitz, Joseph E & Weiss, Andrew, 1981. "Credit Rationing in Markets with Imperfect Information," American Economic Review, American Economic Association, vol. 71(3), pages 393-410, June.
    13. Kim, Hong Sik & Sohn, So Young, 2010. "Support vector machines for default prediction of SMEs based on technology credit," European Journal of Operational Research, Elsevier, vol. 201(3), pages 838-846, March.
    14. Jae Kang & Almas Heshmati, 2008. "Effect of credit guarantee policy on survival and performance of SMEs in Republic of Korea," Small Business Economics, Springer, vol. 31(4), pages 445-462, December.
    15. Young Lee & Changyong Rhee & Taeyoon Sung, 2006. "Fiscal policy in Korea: Before and after the financial crisis," International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 13(4), pages 509-531, August.
    16. Y Kim & S Y Sohn, 2007. "Technology scoring model considering rejected applicants and effect of reject inference," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(10), pages 1341-1347, October.
    17. Moon, Tae Hee & Sohn, So Young, 2008. "Technology scoring model for reflecting evaluator's perception within confidence limits," European Journal of Operational Research, Elsevier, vol. 184(3), pages 981-989, February.
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    1. Yonghan Ju & So Young Sohn, 2015. "Stress test for a technology credit guarantee fund based on survival analysis," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 66(3), pages 463-475, March.
    2. Wang, Huamao & Yang, Zhaojun & Zhang, Hai, 2015. "Entrepreneurial finance with equity-for-guarantee swap and idiosyncratic risk," European Journal of Operational Research, Elsevier, vol. 241(3), pages 863-871.
    3. Lessmann, Stefan & Baesens, Bart & Seow, Hsin-Vonn & Thomas, Lyn C., 2015. "Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research," European Journal of Operational Research, Elsevier, vol. 247(1), pages 124-136.
    4. Bai, Chunguang & Shi, Baofeng & Liu, Feng & Sarkis, Joseph, 2019. "Banking credit worthiness: Evaluating the complex relationships," Omega, Elsevier, vol. 83(C), pages 26-38.
    5. Liu, Zhengchi & Shang, Jennifer & Wu, Shin-yi & Chen, Pei-yu, 2020. "Social collateral, soft information and online peer-to-peer lending: A theoretical model," European Journal of Operational Research, Elsevier, vol. 281(2), pages 428-438.
    6. Ju, Yonghan & Jeon, Song Yi & Sohn, So Young, 2015. "Behavioral technology credit scoring model with time-dependent covariates for stress test," European Journal of Operational Research, Elsevier, vol. 242(3), pages 910-919.

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