Recent Methods from Statistics and Machine Learning for Credit Scoring

Kraus, Anne

ISBN 10: 3954047365 / ISBN 13: 9783954047369

Edité par Cuvillier, 2014

A propos de cet article

N° de réf. du vendeur I-9783954047369

Détails bibliographiques

• Titre: Recent Methods from Statistics and Machine Learning for Credit Scoring
• Éditeur: Cuvillier
• Date de parution: 2014
• Langue: anglais
• ISBN à 10 chiffres: 3954047365
• ISBN à 13 chiffres: 9783954047369
• Reliure: Couverture souple
• État de l'article: New

A propos de ce titre

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.