Recent Methods from Statistics and Machine Learning for Credit Scoring

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• Publisher: Cuvillier Verlag
• Year: 2014
• Print ISBN: 9783954047369
• eBook ISBN: 9783736947368
• Edition: 1

Credit scoring models are the basis for financial institutions like retail and consumer credit banks. The purpose of the models is to evaluate the likelihood of credit applicants defaulting in order to decide whether to grant them credit. The area under the receiver operating characteristic (ROC) curve (AUC) is one of the most commonly used measures to evaluate predictive performance in credit scoring. The aim of this thesis is to benchmark different methods for building scoring models in order to maximize the AUC. While this measure is used to evaluate the predictive accuracy of the presented algorithms, the AUC is especially introduced as direct optimization criterion.

Table of contents

• Abstract
• Zusammenfassung
• Contents
• List of Figures
• List of Tables
• Chapter 1 Introduction
• Chapter 2 Measures of Performance and Data Description
• Chapter 3 Logistic Regression
• Chapter 4 Optimization AUC
• Chapter 5 Generalized Additive Model
• Chapter 6 Recursive Partitioning
• Chapter 7 Boosting
• Chapter 8 Summary and Outlook
• Appendix A Supplementary Material
• Appendix B Computational Aspects
• Bibliography
• Eidesstattliche Versicherung