Credit Strategy Design of Small and Medium-Sized Enterprises

Abstract

Small and medium-sized enterprises play an important role in promoting social and economic growth, which is an important foundation for the development of China’s national economy. From the perspective of bank interests, this paper uses the invoice data of small and medium-sized enterprises to evaluate their credit risk and formulates corresponding bank credit strategies for these enterprises in different industries and properties. Firstly, the credit risk identification factor system is constructed by using feature engineering, and the quantitative model of enterprise credit risk is built based on back propagation (BP) neural network to predict the default probability of enterprises. On this basis, the k-prototypes clustering algorithm is used to classify enterprises. According to the default probability of each enterprise and the loss rate under different interest rates, a nonlinear programming model, with the maximum expected profit of banks as the goal, is constructed. The simulated annealing algorithm is used to obtain the optimal solution of the credit strategy.