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Statistical Classification Methods in Consumer Credit Scoring: a Review
Author(s) -
Hand D. J.,
Henley W. E.
Publication year - 1997
Publication title -
journal of the royal statistical society: series a (statistics in society)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.103
H-Index - 84
eISSN - 1467-985X
pISSN - 0964-1998
DOI - 10.1111/j.1467-985x.1997.00078.x
Subject(s) - confidentiality , context (archaeology) , computer science , actuarial science , statistical analysis , econometrics , machine learning , statistics , business , mathematics , computer security , geography , archaeology
Credit scoring is the term used to describe formal statistical methods used for classifying applicants for credit into ‘good’ and ‘bad’ risk classes. Such methods have become increasingly important with the dramatic growth in consumer credit in recent years. A wide range of statistical methods has been applied, though the literature available to the public is limited for reasons of commercial confidentiality. Particular problems arising in the credit scoring context are examined and the statistical methods which have been applied are reviewed.