Publication details

Assessment of Scoring Models Using Information Value



Year of publication 2010
Type Article in Proceedings
Conference 19th International Conference on Computational Statistics, Paris France, August 22-27, 2010 Keynote, Invited and Contributed Papers
MU Faculty or unit

Faculty of Science

Field Applied statistics, operation research
Keywords Credit scoring; Quality indexes; Information value; Quantiles; Kernel smoothing
Description It is impossible to use a scoring model effectively without knowing how good it is. Quality indexes like Gini, Kolmogorov-Smirnov statistics and Information value are therefore used to assess quality of given scoring model. The paper deals mainly with Information value. Commonly it is computed by discretisation of data into bins using deciles. One constraint is required to be met in this case. Number of cases have to be nonzero for all bins. If this constraint is not fulfilled there are numerous practical procedures for preserving finite results. As an alternative method to empirical estimates we can use the kernel smoothing theory.
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