Personal Credit Risk Assessment Based On Big Data Platform A Case Study Of M Company's Personal Credit Business

The rapid development of Internet finance provides new financing channels for the development of small and micro enterprises and individual entrepreneurship.However,Internet finance is facing such problems as the balance of non-performing loans,the sharp increase of non-performing loans,and the failure of various platforms.Personal credit risk directly restricts the development of Internet finance.Based on this,this paper studies the problem of personal credit risk assessment based on the big data platform,and reveals the relevant factors of personal credit risk and their links.Firstly,according to the actual business big data,the data analysis technology is used to preprocess the actual business big data.Secondly,the XGBoost model is used to construct the large data personal credit risk assessment index system of Internet financial business.The index system has the characteristics of abundant evaluation data items,combination of static data and dynamic data,wide data sources and timeliness,which makes up for the limitations of the classical personal credit risk evaluation index system,such as unsatisfactory population coverage,static data and data authenticity can not be verified.Thirdly,based on the large data index system,a Logistic+XGBoost is constructed.Personal credit risk assessment model.The model can output the importance and correlation coefficients of features,which can be well interpreted and cross-validated with the professional knowledge of the industry to further promote the improvement of personal credit business.Finally,an empirical analysis of M company's personal credit business is carried out,and the proposed personal credit risk management countermeasures are applied.Personal credit risk management countermeasures are divided into four management intervals: low risk of default rate,medium risk of default rate,high risk of default rate and extremely high risk of default rate.The low risk of default rate adopts the management countermeasures of strengthening and continuously optimizing the individual credit risk assessment model;the risk of default rate adopts the management countermeasures of graded audit;the high risk of default rate adopts the management countermeasures of risk pricing;the high risk of default rate adopts the management countermeasures of loan issuance by stages.The empirical results of M company show that it is suitable to apply the low risk management countermeasures of default rate,and the company still needs to continue to invest in large data wind control.Driven by big data and artificial intelligence technology,we can identify individual credit risk more comprehensively and minimize default rate,thereby reducing default losses and improving the profitability of the company.The research results of this project provide theoretical reference and practical reference for promoting the healthy development of Internet financial industry.