Consumer finance data generator - a new approach to Credit Scoring technique comparison
This paper aims to present a general idea of method comparison of Credit Scoring techniques. Any scorecard can be made in various methods based on variable transformations in the logistic regression model. To make a comparison and come up with the proof that one technique is better than another is a big challenge due to the limited availability of data. The same conclusion cannot be guaranteed when using other data from another source. The following research challenge can therefore be formulated: how should the comparison be managed in order to get general results that are not biased by particular data? The solution may be in the use of various random data generators. The data generator uses two approaches: transition matrix and scorings. Here are presented both: results of comparison methods and the methodology of these comparison techniques creating. Before building a new model the modeler can undertake a comparison exercise that aims at identifying the best method in the case of the particular data. Here are presented various measures of predictive model like: Gini, Delta Gini, VIF and Max p-value, emphasizing the multi-criteria problem of a "Good model". The idea that is being suggested is of particular use in the model building process where there are defined complex criteria trying to cover the important problems of model stability over a period of time, in order to avoid a crisis. Some arguments for choosing Logit or WOE approach as the best scorecard technique are presented.
Banking Retail Consumer Finance Data Generator – Credit Scoring Data Repository
This paper presents two cases of random banking data generators based on migration matrices and scoring rules. The banking data generator is a breakthrough in researches aimed at finding a method to compare various credit scoring techniques. These data are very useful for various analyses to understand the complexity of banking processes in a better way and are also of use for students and their researches. Another application can be in the case of small samples, e.g. when historical data are too fresh or are connected with the processing of a small number of exposures. In these cases a data generator can extend a sample to an adequate size for advanced analysis. The influence of one cyclic macro-economic variable on client characteristics and their stability over time is analyzed. Some stimulating conclusions for crisis behavior are presented, namely that if a crisis is impacted by both factors: application and behavioral, then it is very difficult to clearly indicate these factors in a typical scoring analysis and the crisis becomes widespread in every kind of risk report.
Application scoring: logit model approach and the divergence method compared
This study presents the example of application scoring. Two methods are considered: logit model approach and the divergence method. The practical example uses contemporary data on loan applications from the Polish bank. The constructed scoring models are validated on the hold-out sample. Both types of models seem to be acceptable and have high discriminatory power. The prediction accuracy measures indicate that the scoring based on divergence method is better than the one founded on logit model approach.