Non-linearity of scorecard log-odds

Ross A. McDonald, Matthew Sturgess, Keith Smith, Michael S. Hawkins and Edward Xiao-Ming Huang

International Journal of Forecasting, 2012, vol. 28, issue 1, 239-247

Abstract: The use of linear and log-linear models for scorecard construction is nearly universal. In this paper we address the question of non-linearity in the distribution of a scorecard’s inferred log-odds to score relationship. Linear scorecards are excellent and robust ranking tools, but the inferred default probabilities are increasingly used in day-to-day business operations — within account-level strategies, for cutoff setting, and for capital allocation. All of these uses are dependent upon the accurate estimation of the probability of default, which is a quality independent of a model’s ranking performance.

Keywords: Credit scoring; Data mining; Finance; Regression; Scorecard (search for similar items in EconPapers)
Date: 2012
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0169207011000240
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:28:y:2012:i:1:p:239-247

DOI: 10.1016/j.ijforecast.2011.01.001

Access Statistics for this article

International Journal of Forecasting is currently edited by R. J. Hyndman

More articles in International Journal of Forecasting from Elsevier
Bibliographic data for series maintained by Catherine Liu ().