Machine learning method to solve the credit decision-making problem of small and medium-sized enterprises

ABSTRACT

Small and medium-sized enterprises(SMEs) play important roles in our economy. However, the scale of SMEs is relatively small. There is also a lack of pledged assets, which results in a problem of difficulty in borrowing funds. Banks face the problem that how can they determine whether to lend or not when evaluating SMEs credit risk factors, including the strength and reputation, as well as credit strategies, such as loan quota, interest rate and term. In this paper, support vector machine and decision tree method are comprehensively used to learn the enterprises data and evaluate the credit rating of enterprises lacking credit information. A linear optimization model is established based on the bank's principle of maximizing the expected annual profit, and this paper provides the optimal strategy for banks to decide the amount of loans granted to each enterprise. In addition, emergency situation is taken as an example, such as Covid-19 epidemic, by utilizing machine learning method and optimization theory, based on the fact that banks expect to maximize profit, establish an optimization model with wider applicability. And this paper provides credit strategy for banks when facing unexpected environmental emergency. © 2021 SPIE.