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Spatial dependence in credit risk and its improvement in credit scoring

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  • Fernandes, Guilherme Barreto
  • Artes, Rinaldo

Abstract

Credit scoring models are important tools in the credit granting process. These models measure the credit risk of a prospective client based on idiosyncratic variables and macroeconomic factors. However, small and medium sized enterprises (SMEs) are subject to the effects of the local economy. From a data set with the localization and default information of 9 million Brazilian SMEs, provided by Serasa Experian (the largest Brazilian credit bureau), we propose a measure of the local risk of default based on the application of ordinary kriging. This variable has been included in logistic credit scoring models as an explanatory variable. These models have shown better performance when compared to models without this variable. A gain around 7 percentage points of KS and Gini was observed.

Suggested Citation

  • Fernandes, Guilherme Barreto & Artes, Rinaldo, 2016. "Spatial dependence in credit risk and its improvement in credit scoring," European Journal of Operational Research, Elsevier, vol. 249(2), pages 517-524.
  • Handle: RePEc:eee:ejores:v:249:y:2016:i:2:p:517-524
    DOI: 10.1016/j.ejor.2015.07.013
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    1. Jos魍ar, 2012. "Space-time approach to commercial property prices valuation," Applied Economics, Taylor & Francis Journals, vol. 44(28), pages 3705-3715, October.
    2. Giesecke, Kay & Weber, Stefan, 2006. "Credit contagion and aggregate losses," Journal of Economic Dynamics and Control, Elsevier, vol. 30(5), pages 741-767, May.
    3. Wiginton, John C., 1980. "A Note on the Comparison of Logit and Discriminant Models of Consumer Credit Behavior," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 15(3), pages 757-770, September.
    4. Barro, Diana & Basso, Antonella, 2010. "Credit contagion in a network of firms with spatial interaction," European Journal of Operational Research, Elsevier, vol. 205(2), pages 459-468, September.
    5. D. J. Hand & W. E. Henley, 1997. "Statistical Classification Methods in Consumer Credit Scoring: a Review," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 160(3), pages 523-541, September.
    6. Steven C. Bourassa & Eva Cantoni & Martin Hoesli, 2010. "Predicting House Prices with Spatial Dependence: A Comparison of Alternative Methods," Journal of Real Estate Research, American Real Estate Society, vol. 32(2), pages 139-160.
    7. Gary A. Dymski, 2006. "Discrimination in the Credit and Housing Markets: Findings and Challenges," Chapters, in: William M. Rodgers III (ed.), Handbook on the Economics of Discrimination, chapter 8, Edward Elgar Publishing.
    8. Orgler, Yair E, 1970. "A Credit Scoring Model for Commercial Loans," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 2(4), pages 435-445, November.
    9. Dubin, Robin A., 1992. "Spatial autocorrelation and neighborhood quality," Regional Science and Urban Economics, Elsevier, vol. 22(3), pages 433-452, September.
    10. Sumit Agarwal, 2010. "Distance and Private Information in Lending," Review of Financial Studies, Society for Financial Studies, vol. 23(7), pages 2757-2788, July.
    11. Stefan Weber & Kay Giesecke, 2003. "Credit Contagion and Aggregate Losses," Computing in Economics and Finance 2003 246, Society for Computational Economics.
    12. Sumit Agarwal & Brent W. Ambrose & Souphala Chomsisengphet & Anthony B. Sanders, 2012. "Thy Neighbor’s Mortgage: Does Living in a Subprime Neighborhood Affect One’s Probability of Default?," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 40(1), pages 1-22, March.
    13. Zambaldi, Felipe & Aranha, Francisco & Lopes, Hedibert & Politi, Ricardo, 2011. "Credit granting to small firms: A Brazilian case," Journal of Business Research, Elsevier, vol. 64(3), pages 309-315, March.
    14. David Durand, 1941. "Risk Elements in Consumer Instalment Financing," NBER Books, National Bureau of Economic Research, Inc, number dura41-1, december.
    15. Anjali Kumar & Ajai Nair & Adam Parsons & Eduardo Urdapilleta, 2006. "Expanding Bank Outreach through Retail Partnerships : Correspondent Banking in Brazil," World Bank Publications - Books, The World Bank Group, number 7038, December.
    16. Peter Kolesar & Janet L. Showers, 1985. "A Robust Credit Screening Model Using Categorical Data," Management Science, INFORMS, vol. 31(2), pages 123-133, February.
    17. David Durand, 1941. "Risk Elements in Consumer Instalment Financing, Technical Edition," NBER Books, National Bureau of Economic Research, Inc, number dura41-2, december.
    18. Scalera, Domenico & Zazzaro, Alberto, 2001. "Group reputation and persistent (or permanent) discrimination in credit markets," Journal of Multinational Financial Management, Elsevier, vol. 11(4-5), pages 483-496, December.
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