Title: Recent Methods from Statistics and Machine Learning for Credit Scoring (Paperback or Softback)
Publisher: Cuvillier 7/8/2014
Publication Date: 2014
Language: English
ISBN 10: 3954047365
ISBN 13: 9783954047369
Binding: Paperback or Softback
Condition: New
Weight: 0.45 pounds
Book Type: Book

About this title

Synopsis

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.

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