Exploration of Commercial Banks’ Credit Business in the Context of Big Data

Abstract

With the development of big data technology, there are many problems in the credit system of commercial banks, commercial banks continue to improve the credit system to better ensure the good development of loan business. Micro, Small, and Medium Enterprises (Msmes) are the foundation of national economy, it is very important to study the financing of msmes, commercial banks should make correct credit decision for different msmes. In this paper, we solve the problem of commercial bank making loan decision to loan enterprise from the source, and study the efficient decision-making method. Based on RFM model, we propose a new YNA model to determine the main value of the studied credit customers, k-means clustering algorithm is used to determine the stratification of certain type of enterprises, KNN model and logistic regression model is used to help banks to identify the risk of msmes. It is found that the use of big data technology to facilitate bank credit decision-making improves the efficiency and accuracy of commercial bank credit business review of different enterprises. Based on the essence of commercial bank credit business model, this paper combines the characteristics of commercial bank credit business and big data through the financing of small and medium-sized enterprises.