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Recent Methods from Statistics and Machine Learning for Credit Scoring

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Recent Methods from Statistics and Machine Learning for Credit Scoring (English shop)

Anne Kraus (Author)


Table of Contents, PDF (45 KB)
Extract, PDF (160 KB)

ISBN-13 (Hard Copy) 9783954047369
ISBN-13 (eBook) 9783736947368
Language English
Page Number 166
Lamination of Cover matt
Edition 1. Aufl.
Publication Place Göttingen
Place of Dissertation München
Publication Date 2014-07-08
General Categorization Dissertation
Departments Mathematics
Keywords Credit Scoring, AUC, optimization, Banking

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.