Predictive Power Measures for Scoring Models
| Authors | |
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| Year of publication | 2010 |
| Type | Appeared in Conference without Proceedings |
| MU Faculty or unit | |
| Citation | |
| Description | Credit scoring models are widely used to predict a probability of an event like client's default. To measure the quality of the scoring models it is possible to use quantitative indexes such as Gini index, K-S statistics, C-statistics and Lift. They are used for comparison of several developed models at the moment of development as well as for monitoring of quality of those models after deployment into real business. The paper deals with mentioned quality indexes, their properties and relationships. The main contribution of the paper is proposition and discussion of indexes and curves based on Lift. Curve of ideal Lift is defined, Lift ratio is proposed as analogy to Gini index. Integrated Relative Lift is defined and discussed. Also the problem of estimation of Information value is mentioned. 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. Finally, a new approach to measure power of scoring models is discussed and a new quality index is proposed. A simulation study compares it with other quality indexes. |
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