Research on credit risk assessment based on machine learning

Due to the large differences in Internet technology and data application among financial institutions in China, some city commercial banks and rural commercial banks are still in the initial stage of exploration in the technical application of internet credit loan business, and their default risk management capabilities are weak and difficult to achieve. Effectively identify the applicant's risk of default. Aiming at the problem of credit risk, on the basis of studying the methods of prevention and control of pre-loan risk, this paper uses various methods such as coding and variables to process credit data. The random forest method is integrated to select variables, and the weighted logistic regression method is used to construct a pre-loan risk early warning model, which can perceive credit risks in advance and give early warnings. On the basis of studying random forest algorithm and parameter optimization method, a post-loan risk early warning model based on random forest algorithm is designed; and the model parameters are optimized to make the risk early warning model have high accuracy. Subsequently, this paper uses about 900,000 credit data of Lending Club in 2016 and 2017 as samples, and divides user data sets that are easier to classify based on the user level identified by Lending Club, and then uses the machine learning model to evaluate and verify the results. The effectiveness of selected models in credit risk assessment scenarios.