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.

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.