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Performance Ranking of Czech Credit Scoring Models
Smolár, Peter ; Havránek, Tomáš (advisor) ; Jakubík, Petr (referee)
This thesis provides a comprehensive ranking of 11 Czech statistical and 4 foreign credit scoring models. The ranking is based on the predictive performance of individual models, as measured by the area under curve, evaluated on a randomly sampled set of 250 training and validation samples. After establishing a baseline comparison, 3 avenues of estimation setup optimization are explored, namely missing value treatment, estimation method and the use of additional non-financial variables. After being optimized, the models are once again ranked based on their predictive performance. Statistical inference is drawn using ANOVA and the Friedman test, along with the corresponding Tukey and Nemeyi pos-hoc tests. In their baseline form, the Czech credit scoring models are found to be outperformed by the foreign benchmark model. Treating the missing values by OLS imputation and estimating the models by probit, significantly is found to significantly improve their predictive performance. In their optimized form, the difference in predictive performance between Czech and foreign credit scoring model is found to be only marginal. JEL Classification G28, G32, G33, G38 Keywords credit scoring, multiple discriminant analysis, logit analysis, probit analysis Author's e-mail 71247263@fsv.cuni.cz Supervisor's e-mail...
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Building credit scoring models using selected statistical methods in R
Jánoš, Andrej ; Bašta, Milan (advisor) ; Pecáková, Iva (referee)
Credit scoring is important and rapidly developing discipline. The aim of this thesis is to describe basic methods used for building and interpretation of the credit scoring models with an example of application of these methods for designing such models using statistical software R. This thesis is organized into five chapters. In chapter one, the term of credit scoring is explained with main examples of its application and motivation for studying this topic. In the next chapters, three in financial practice most often used methods for building credit scoring models are introduced. In chapter two, the most developed one, logistic regression is discussed. The main emphasis is put on the logistic regression model, which is characterized from a mathematical point of view and also various ways to assess the quality of the model are presented. The other two methods presented in this thesis are decision trees and Random forests, these methods are covered by chapters three and four. An important part of this thesis is a detailed application of the described models to a specific data set Default using the R program. The final fifth chapter is a practical demonstration of building credit scoring models, their diagnostics and subsequent evaluation of their applicability in practice using R. The appendices include used R code and also functions developed for testing of the final model and code used through the thesis. The key aspect of the work is to provide enough theoretical knowledge and practical skills for a reader to fully understand the mentioned models and to be able to apply them in practice.
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Selection Bias Reduction in Credit Scoring Models
Ditrich, Josef ; Hebák, Petr (advisor) ; Pecáková, Iva (referee) ; Zamrazilová, Eva (referee)
Nowadays, the use of credit scoring models in the financial sector is a common practice. Credit scoring plays an important role in profitability and transparency of lending business. Given the high credit volumes, even a small improvement of discriminatory and predictive power of a credit scoring model may provide a substantial additional profit. Scoring models are applied on the through-the-door population, however, for creating them or adjusting already existing credit rules, it is usual to use only the data corresponding to accepted applicants for which payment discipline can be observed. This discrepancy can lead to reject bias (or selection bias in general). Methods trying to eliminate or reduce this phenomenon are known by the term reject inference. In general, these methods try to assess the behavior of rejected applicants or to obtain an additional information about them. In the dissertation thesis, I dealt with the enlargement method which is based on a random acceptance of applicants that would have been rejected. This method is not only time consuming but also expensive. Therefore I looked for the ways how to reduce the cost of acquiring additional information about rejected applicants. As a result, I have proposed a modification which I called the enlargement method with sorting variable. It was validated on real bank database with two possible sorting variables and the results were compared with the original version of the method. It was shown that both tested approaches can reduce its cost while retaining the accuracy of the scoring models.
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