Credit rating with a monotonicity-constrained support vector machine model

Chih Chuan Chen, Sheng Tun Li

Research output: Contribution to journalArticle

29 Citations (Scopus)

Abstract

Deciding whether borrowers can fulfill their obligations is a major issue for financial institutions, and while various credit rating models have been developed to help achieve this, they cannot reflect the domain knowledge of human experts. This paper proposes a new rating model based on a support vector machine with monotonicity constraints derived from the prior knowledge of financial experts. Experiments conducted on real-world data sets show that the proposed method, not only data driven but also domain knowledge oriented, can help correct the loss of monotonicity in data occurring during the collecting process, and performs better than the conventional counterpart.

Original languageEnglish
Pages (from-to)7235-7247
Number of pages13
JournalExpert Systems With Applications
Volume41
Issue number16
DOIs
Publication statusPublished - 2014 Nov 15

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Support vector machines
Experiments

All Science Journal Classification (ASJC) codes

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence

Cite this

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Credit rating with a monotonicity-constrained support vector machine model. / Chen, Chih Chuan; Li, Sheng Tun.

In: Expert Systems With Applications, Vol. 41, No. 16, 15.11.2014, p. 7235-7247.

Research output: Contribution to journalArticle

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