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What Triggers Loan Repayment Failure of Consumer Loans – Evidence from Bosnia and Herzegovina

Author

Listed:
  • Sanela Pasic

    (Sarajevo School of Science and Technology, Bosnia and Herzegovina)

  • Adisa Omerbegovic Arapovic

    (Sarajevo School of Science and Technology, Bosnia and Herzegovina)

Abstract

This research explores most dominant lending product to population of Bosnia and Herzegovina, a consumer loan, with aim to answer the question of what factors trigger loan repayment failure. It explores relation of borrower characteristics such as gender, age, level of indebtness to likeliness of loan repayment by use of probit on banking data sample representing 39% of the market share in the country. It identifies factors which lead to loan repayment failure and also provides exact empirical model for default prediction at loan approval stage. Main audience of this research should be banks, which could use the finding of the study to adjust their credit policies and risk appetite to ensure that lending losses from this strongly present product are minimized, thus leading to stable and financially sound banking sector.

Suggested Citation

  • Sanela Pasic & Adisa Omerbegovic Arapovic, 2016. "What Triggers Loan Repayment Failure of Consumer Loans – Evidence from Bosnia and Herzegovina," Eurasian Journal of Business and Management, Eurasian Publications, vol. 4(1), pages 11-22.
  • Handle: RePEc:ejn:ejbmjr:v:4:y:2016:i:1:p:11-22
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    References listed on IDEAS

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    1. Anderson, Raymond, 2007. "The Credit Scoring Toolkit: Theory and Practice for Retail Credit Risk Management and Decision Automation," OUP Catalogue, Oxford University Press, number 9780199226405.
    2. 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.
    3. 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.
    4. Edward C. Norton & Hua Wang & Chunrong Ai, 2004. "Computing interaction effects and standard errors in logit and probit models," Stata Journal, StataCorp LP, vol. 4(2), pages 154-167, June.
    5. Tyrone T. Lin & Chia-Chi Lee & Chun-Hung Chen, 2009. "Impacts of the borrower's attributes, loan contract contents, and collateral characteristics on mortgage loan default," The Service Industries Journal, Taylor & Francis Journals, vol. 31(9), pages 1385-1404, October.
    6. 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.
    7. David Durand, 1941. "Risk Elements in Consumer Instalment Financing," NBER Books, National Bureau of Economic Research, Inc, number dura41-1.
    8. J. Scott Long & Jeremy Freese, 2006. "Regression Models for Categorical Dependent Variables using Stata, 2nd Edition," Stata Press books, StataCorp LP, edition 2, number long2, March.
    9. L C Thomas & R W Oliver & D J Hand, 2005. "A survey of the issues in consumer credit modelling research," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(9), pages 1006-1015, September.
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