Abstract
The arrival of the era of big data has provided a new direction of development for internet financial credit collection. First of all, the article introduced the situation of internet finance and traditional credit industry. Based on that, the mathematical model was used to demonstrate the necessity of developing big data financial credit information. Then, the Internet financial credit data are preprocessed, the variables suitable for modeling are selected, and the dynamic credit tracking model of BP neural network based on adaptive genetic algorithm is constructed. It is found that both LM training algorithm and Bayesian algorithm can converge the error to 10e-6 quickly in the model training, and the overall training effect is ideal. Finally, the rule extraction algorithm is used to simulate the test samples. The accuracy rate of each sample method is over 90%, and some accuracy rate is even more than 90%, which indicates that the model is applicable to the credit data of big data in internet finance.
Highlights
The credit system is the cornerstone to the development of the market economy and the financial industry
The optimization process of the traditional genetic algorithm is as follows [19]: (1) According to the characteristics of the problem to be dealt with, select the code corresponding to the problem solution, and give an initial population, which includes N chromosomes
To test the dynamic credit tracking model established above, the remaining 8 test samples were input into the trained network for testing
Summary
The credit system is the cornerstone to the development of the market economy and the financial industry. Many researchers have put forward some creative ideas for the credit reporting system of big data [2]. Due to the fact that large data credit is a brand-new concept, there are relatively few literatures directly related to it, and the related research lacks systematisms and depth. Under the background of big data of Internet finance and domestic traditional credit investigation industry, the construction of personal credit investigation system is discussed. Based on economic theory, concepts of credit information costs and potential risk costs are raised and the use of quantitative models to demonstrate the necessity of developing big data credits. A credit-tracking model based on big data was constructed.
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