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The advent of the era of big data has provided a new way of development for Internet financial credit collection. The traditional methods of credit risk identification of Internet financial enterprises cannot get the characteristics of credit risk zoning, leading to large errors in the results of credit risk identification. Therefore, this paper proposes a new method of credit risk identification based on big data for Internet financial enterprises. According to the big data perspective, the credit risk assessment steps of Internet financial enterprises are analyzed and the weight of assessment indicators is calculated using the improved analytic hierarchy process (AHP), and the linear weighted synthesis method is applied to comprehensively assess the credit of clients. Using the unique characteristics of big data credit risk region division, the big data credit risk is determined by rule-based matching method. The eXtreme Gradient Boosting (XGBoost) machine learning algorithm is used to establish a credit risk identification model of Internet financial enterprises. The kappa coefficient and ROC curve are used to evaluate the performance of the proposed method. Experimental results show that the proposed method can accurately assess the credit risk of Internet financial enterprises.

Research on Credit Risk Identification of Internet Financial Enterprises Based on Big Data