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Does reject inference really improve the performance of application scoring models? · Study

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ArticleDoes reject inference really improve the performance of application scoring models?

Jonathan N Crook2003

OA

Articlegreen2003

Does reject inference really improve the performance of application scoring models?

Jonathan N Crook · John Banasik

88 citations·0/0 files stored·Journal of Banking & Finance

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Financial Distress and Bankruptcy Prediction Forecasting Techniques and Applications Imbalanced Data Classification Techniques

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References

2002Credit Scoring and Its Applications 2017Credit Scoring and Its Applications 1989An econometric analysis of the bank credit scoring problem 1983An Examination of the Conceptual Issues Involved in Developing Credit-Scoring Models 2003Sample selection bias in credit scoring models 1978Credit Scoring and the Equal Credit Opportunity Act

Cited by

2018Statistical Challenges of Administrative and Transaction Data 2011Credit scoring for microfinance: is it worth it?2009Enterprise risk management: small business scorecard analysis 2018A review of credit scoring research in the age of Big Data 2007Reject inference, augmentation, and sample selection 2006Are Securitizations in Substance Sales or Secured Borrowings? Capital‐Market Evidence*2013Alternative bankruptcy prediction models using option-pricing theory 2018A new approach for reject inference in credit scoring using kernel-free fuzzy quadratic surface support vector machines

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Year2003

Typearticle

Published2003-12-04

VenueJournal of Banking & Finance

ISSN-L0378-4266

Languageen

Citations88

Files0 public / 0

ShelfFinancial Distress and Bankruptcy Prediction

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doi10.1016/j.jbankfin.2003.10.010

openalex_workW2070533641

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