Abstract
With the advent of the era of big data, the traditional credit reference business integrates with big data deeply. The traditional credit data processing methods can't accurately analyze the massive credit data in the current credit market. In the future, the requirements for big data processing and analysis capabilities in the credit field will continue to increase. Based on Logistic Regression, this paper establishes a prediction model for individual borrowers' credit risk, and constructs a series of parameters as individual credit risk evaluation indicators to demonstrate the rationality and effectiveness of the model. The study found that through the analysis of credit big data algorithm using the Logistic regression model, the results obtained can accurately assess the credit status of individual borrowers, and then guide financial institutions such as commercial banks to avoid credit risks.
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