Credit rating with a monotonicity-constrained support vector machine model

•We proposed a novel monotonicity constrained SVM model for credit rating.•We evaluate the performance of the model with real-world data sets.•The proposed method can correct the loss of monotonicity in the data.•The proposed method can improve the performance as compared to the conventional SVM. De...

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Bibliographic Details
Published in:Expert systems with applications 2014-11, Vol.41 (16), p.7235-7247
Main Authors: Chen, Chih-Chuan, Li, Sheng-Tun
Format: Article
Language:English
Subjects:
Applied sciences
Computer science
control theory
systems
Credit rating
Data mining
Data processing. List processing. Character string processing
Exact sciences and technology
Expert systems
Human
Memory organisation. Data processing
Monotonicity constraint
Operational research and scientific management
Operational research. Management science
Portfolio theory
Prior domain knowledge
Ratings
Software
Support vector machines
SVM
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Summary:•We proposed a novel monotonicity constrained SVM model for credit rating.•We evaluate the performance of the model with real-world data sets.•The proposed method can correct the loss of monotonicity in the data.•The proposed method can improve the performance as compared to the conventional SVM. 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.
ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2014.05.035