Skip to main content
SpringerLink
Log in

ESIS2: Information Value Estimator for Credit Scoring Models

  • Published:
Computational Economics Aims and scope Submit manuscript

Abstract

Information value is widely used to assess discriminatory power of credit scoring models, i.e. models that try to predict a probability of client’s default. Moreover it is very often used to assess the discriminatory power of variables that enter into these models. This means that the Information value is used as a filter for variable selection. However, empirical estimate using deciles of scores, which is the common way how to compute it, may lead to strongly biased results. The main aim of this paper is to give an alternative estimator of the information value, named ESIS2, which leads to lowered bias and mean square error. The implication of this is better credit scoring model. And what is essential, the direct consequence of having better credit scoring model is significantly higher profitability of credit business.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  • Anderson, R. (2007). The credit scoring toolkit: Theory and practice for retail credit risk management and decision automation. Oxford: Oxford University Press.

    Google Scholar 

  • Crook, J. N., Edelman, D. B., & Thomas, L. C. (2007). Recent developments in consumer credit risk assessment. European Journal of Operational Research, 183(3), 1447–1465.

    Article  Google Scholar 

  • Desai, V. S., Crook, J. N., & Overstreet, G. A, Jr. (1996). A comparison of neural networks and linear scoring models in the credit union environment. European Journal of Operational Research, 95(1), 24–37.

    Article  Google Scholar 

  • Hand, D. J., & Henley, W. E. (1997). Statistical classification methods in consumer credit scoring: A review. Journal of the Royal Statistical Society, Series A., 160(3), 523–541.

    Article  Google Scholar 

  • Johnson, N. L., Kotz, S., & Balakrishnan, N. (1994). Continuous univariate distributions (2nd ed., Vol. 1). New York: Wiley.

    Google Scholar 

  • Koláček, J., & Řezáč, M. (2010). Assessment of scoring models using information value. In Proceedings of Compstat’ 2010 (pp. 1191–1198). Paris.

  • Kundu, D., & Manglick, A. (2005). Discriminating between the log-normal and gamma distributions. Journal of the Applied Statistical Sciences, 14, 175–187.

    Google Scholar 

  • Hsieh, Nan-Chen. (2005). Hybrid mining approach in the design of credit scoring models. Expert Systems with Applications, 28(4), 655–665.

    Article  Google Scholar 

  • Hsieh, Nan-Chen, & Hung, Lun-Ping. (2010). A data driven ensemble classifier for credit scoring analysis. Expert Systems with Applications, 37(1), 534–545.

    Article  Google Scholar 

  • Řezáč, M. (2011). Measuring quality of scoring models using information value. Journal of Communication and Computer, 8(3), 234–239.

    Google Scholar 

  • Řezáč, M. & Koláček, J. (2011). Computation of information value for credit scoring models. In Workshop of the Jaroslav Hájek Center and Financial Mathematics in Practice I. Book of short papers. Jedovnice pp. 75–84.

  • Řezáč, M., & Řezáč, F. (2011). How to measure the quality of credit scoring models. Finance a úvěr: Czech Journal of Economics and Finance, 61(5), 486–507.

    Google Scholar 

  • Siddiqi, N. (2006). Credit risk scorecards: developing and implementing intelligent credit scoring. Hoboken, NJ: Wiley.

    Google Scholar 

  • Thomas, L. C. (2009). Consumer credit models: pricing, profit, and portfolio. Oxford: Oxford University Press.

    Book  Google Scholar 

  • Thomas, L. C., Edelman, D. B., & Crook, J. N. (2002). Credit scoring and its applications. Philadelphia, PA: SIAM Monographs on Mathematical Modeling and Computation.

    Book  Google Scholar 

  • Wiens, B. L. (1999). When log-normal and gamma models give different results: A case study. The American Statistician, 53(2), 89–93.

    Google Scholar 

  • Wilkie, A. D. (2004). Measures for comparing scoring systems. In L. C. Thomas, D. B. Edelman, & J. N. Crook (Eds.), Readings in credit scoring (pp. 51–62). Oxford: Oxford University Press.

Download references

Author information

Authors and Affiliations

  1. Department of Mathematics and Statistics, Faculty of Science, Masaryk University, Kotlářská 2, 611 37 , Brno, Czech Republic

    Martin Řezáč

Authors
  1. Martin Řezáč
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Martin Řezáč.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Řezáč, M. ESIS2: Information Value Estimator for Credit Scoring Models. Comput Econ 45, 303–322 (2015). https://doi.org/10.1007/s10614-014-9424-0

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10614-014-9424-0

Keywords

Search

Navigation