Adjusted Empirical Estimate of Information Value for Credit Scoring Models
| Authors | |
|---|---|
| Year of publication | 2011 |
| Type | Article in Proceedings |
| Conference | PROCEEDINGS ASMDA 2011 |
| MU Faculty or unit | |
| Citation | |
| Field | Applied statistics, operation research |
| Keywords | Credit scoring; Quality indexes; Information value; ESIS |
| Description | To measure the quality of scoring models it is possible to use quantitative indexes such as Gini index, K-S and Information value. The paper deals with the Information value, mainly with issues connected to its computation. The classical way of computation, i.e. empirical estimate using deciles of scores, is easy to implement, but may lead to strongly biased results. Kernel estimate or empirical estimates with supervised interval selection (ESIS) seems to be more appropriate to use. The main contribution of this paper is a proposal of an adjusted procedure for estimation of the Information value. It is based on ideas of ESIS with adjustment for choice of required number of observations in constructed intervals. The properties of all listed Information value estimators are discussed in the simulation study. |
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