Research on user Credit score Model based on Fusion Neural network

The research of this topic is based on financial risk control, and Sunshine Jinke's risk prevention and control is taken as a specific research case. The data set uses user consumption data used by Sunshine Jinke. The data source is a bank's customer consumer loan records in the past five years. The feature fields are extracted according to the degree of correlation between the data and the repayment rate, and combined with the convolutional neural network and the recurrent neural network, these feature fields are processed for the differentiation of credit data and fraud data, and finally matched with user information and scored, matching The process uses the singular matrix factorization idea. Through experimental papers, this idea has good stability and accuracy in the field of risk control forecasting.