![Does reject inference really improve the performance of application scoring models? [An article from: Journal of Banking and Finance]](https://www.ebooknetworking.net/books/B00/0RR/bigB000RR1244.jpg)
Does reject inference really improve the performance of application scoring models? [An article from: Journal of Banking and Finance]
Book Details
Author(s)
J. Crook, J. Banasik
Publisher
Elsevier
ISBN / ASIN
B000RR1244
ISBN-13
978B000RR1245
Availability
Available for download now
Marketplace
United States 🇺🇸
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Description
This digital document is a journal article from Journal of Banking and Finance, published by Elsevier in 2004. The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.
Description:
The parameters of application scorecards are usually estimated using a sample that excludes rejected applicants which may prove biased when applied to all applicants. This paper uses a rare sample that includes those who would normally be rejected to examine the extent to which (1) the exclusion of rejected applicants undermines the predictive performance of a scorecard based only on accepted applicants, and (2) reject inference techniques can remedy the influence of this exclusion.
Description:
The parameters of application scorecards are usually estimated using a sample that excludes rejected applicants which may prove biased when applied to all applicants. This paper uses a rare sample that includes those who would normally be rejected to examine the extent to which (1) the exclusion of rejected applicants undermines the predictive performance of a scorecard based only on accepted applicants, and (2) reject inference techniques can remedy the influence of this exclusion.
