Sinopsis
Lenders use rating and scoring models to rank credit applicants on their expected performance. The models and approaches are numerous. We explore the possibility that estimates generated by models developed with data drawn solely from extended loans are less valuable than they should be because of selectivity bias. We investigate the value of “reject inference” – methods that use a rejected applicant’s characteristics, rather than loan performance data, in scoring model development. In the course of making this investigation, we also discuss the advantages of using parametric as well as nonparametric modeling. These issues are discussed and illustrated in the context of a simple stylized model.
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Reseña del editor
Lenders use rating and scoring models to rank credit applicants on their expected performance. The models and approaches are numerous. We explore the possibility that estimates generated by models developed with data drawn solely from extended loans are less valuable than they should be because of selectivity bias. We investigate the value of “reject inference” – methods that use a rejected applicant’s characteristics, rather than loan performance data, in scoring model development. In the course of making this investigation, we also discuss the advantages of using parametric as well as nonparametric modeling. These issues are discussed and illustrated in the context of a simple stylized model.
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Specification and Informational Issues in Credit Scoring
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CreateSpace Independent Publishing Platform, 2014
ISBN 10: 1505308836
ISBN 13: 9781505308839
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Specification and Informational Issues in Credit Scoring
Publicado por
Createspace Independent Publishing Platform, 2014
ISBN 10: 1505308836
ISBN 13: 9781505308839
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Specification and Informational Issues in Credit Scoring (Paperback)
Publicado por
Createspace Independent Publishing Platform, 2014
ISBN 10: 1505308836
ISBN 13: 9781505308839
Nuevo
Paperback
Librería: CitiRetail, Stevenage, Reino Unido
Calificación del vendedor: 5 de 5 estrellas
Paperback. Condición: new. Paperback. Lenders use rating and scoring models to rank credit applicants on their expected performance. The models and approaches are numerous. We explore the possibility that estimates generated by models developed with data drawn solely from extended loans are less valuable than they should be because of selectivity bias. We investigate the value of "reject inference" - methods that use a rejected applicant's characteristics, rather than loan performance data, in scoring model development. In the course of making this investigation, we also discuss the advantages of using parametric as well as nonparametric modeling. These issues are discussed and illustrated in the context of a simple stylized model. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Nº de ref. del artículo: 9781505308839
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