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Bank Lending Policy, Credit Scoring and the Survival of Loans

  • Roszbach, Kasper


    (Dept. of Economics, Stockholm School of Economics)

To evaluate loan applicants, banks use a large variety of systems. The objective of such credit scoring models typically is to minimize default rates or the number of incorrectly classified loans. Thereby they fail to take into account that loans are multiperiod contracts. From a utility maximizing perspective it is not only important to know if but also when a loan will default. In this paper a Tobit model with a variable censoring threshold and sample selection effects is estimated for (1) the decision to provide a loan or not and (2) the survival of granted loans. The model is shown to be an affective tool to separate applicants with short survival times from those with long survivals The bank´s loan provision process is shown to be inefficient. Loans are granted in a way that conflicts with both default risk minimization and survival time maximization. There is thus no trade-off between higher default risk and higher return in the policy of banks.

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Paper provided by Stockholm School of Economics in its series SSE/EFI Working Paper Series in Economics and Finance with number 261.

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Length: 28 pages
Date of creation: 29 Sep 1998
Date of revision:
Publication status: Forthcoming in The Review of Economics and Statistics.
Handle: RePEc:hhs:hastef:0261
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  1. Townsend, Robert M, 1982. "Optimal Multiperiod Contracts and the Gain from Enduring Relationships under Private Information," Journal of Political Economy, University of Chicago Press, vol. 90(6), pages 1166-86, December.
  2. Gale, Douglas & Hellwig, Martin, 1985. "Incentive-Compatible Debt Contracts: The One-Period Problem," Review of Economic Studies, Wiley Blackwell, vol. 52(4), pages 647-63, October.
  3. Jacobson, Tor & Roszbach, Kasper, 2003. "Bank lending policy, credit scoring and value-at-risk," Journal of Banking & Finance, Elsevier, vol. 27(4), pages 615-633, April.
  4. Shumway, Tyler, 2001. "Forecasting Bankruptcy More Accurately: A Simple Hazard Model," The Journal of Business, University of Chicago Press, vol. 74(1), pages 101-24, January.
  5. Dionne, Georges & Artis, Manuel & Guillen, Montserrat, 1996. "Count data models for a credit scoring system," Journal of Empirical Finance, Elsevier, vol. 3(3), pages 303-325, September.
  6. Stephen D. Williamson, 1984. "Costly Monitoring, Loan Contracts and Equilibrium Credit Rationing," Working Papers 572, Queen's University, Department of Economics.
  7. Carling, Kenneth & Jacobson, Tor & Roszbach, Kasper, 2001. "Dormancy risk and expected profits of consumer loans," Journal of Banking & Finance, Elsevier, vol. 25(4), pages 717-739, April.
  8. 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.
  9. Boyes, William J. & Hoffman, Dennis L. & Low, Stuart A., 1989. "An econometric analysis of the bank credit scoring problem," Journal of Econometrics, Elsevier, vol. 40(1), pages 3-14, January.
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