Matches in SemOpenAlex for { <https://semopenalex.org/work/W2158106558> ?p ?o ?g. }
- W2158106558 abstract "Consider a bank which wishes to decide whether a credit applicant will obtain credit or not. The bank has to assess if the applicant will be able to redeem the credit. This is done by estimating the probability that the applicant will default prior to the maturity of the credit. To estimate this probability of default it is first necessary to identify criteria which separate the “good” from the “bad” creditors, such as loan amount and age or factors concerning the income of the applicant. The question then arises of how a bank identifies a sufficient number of selective criteria that posses the necessary discriminatory power. As a solution, many traditional binary classification methods have been proposed with varying degrees of success. However, a particular problem with credit scoring is that defaults are only observed for a small subsample of applicants. An imbalance exists between the ratio of non-defaulters to defaulters. This has an adverse effect on the aforementioned binary classification methods. Recently one-class classification approaches have been proposed to address the imbalance problem. The purpose of this literature review is threefold: (i)" @default.
- W2158106558 created "2016-06-24" @default.
- W2158106558 creator A5044738212 @default.
- W2158106558 creator A5067047073 @default.
- W2158106558 creator A5084251444 @default.
- W2158106558 date "2009-01-01" @default.
- W2158106558 modified "2023-09-27" @default.
- W2158106558 title "Low-Default Portfolio/One-Class Classification: A Literature Review" @default.
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