Research On The Individual Credit Risk Control Based On Big Data For Online P2P Lending Platform

In the era of big data,with the rapid growth of the Internet finance industry,numerous online financial service channels has occured and the financial services have become increasingly diverse.As a result,the manaagment of P2 P online lending platforms faces greater challenges than ever before.The understanding of individual behavior patterns,financial risks and the new trends in economic development of the P2 P online lending platform demands to be re-established.Under such circumstances,the credit risk control model and system for the internet lending platform need to be reconstructed and optimized,as the risks faced by the online lending platform are becoming increasingly complex and diversified.Therefore,in this study,we further analyze the credit risk control model based on the big data technology,which is applied for P2 P online lending platform.First,the current situation and major challenges faced by the P2 P online lending platform in the context of big data has been analyzed,and then the difficulties from the perspectives of current sutuation and methods of traditional risk control has also been studied respectively.Meanwhile,the diversity of financial data brought about by the rapid development of Internet finance and its impact on traditional risk control models have been studied.Based on the theory of information asymmetry,it gives a theoretical explanation for the matching raito of the lending platform.For the traditional risk control model,we focused on the analysis of its objectivity of the designing and the exporation of underlying information,which leads to the solution of the above problems.Regarding the current situaion of the first problem,the dilemma caused by data diversity and information asymmetry,a credit evaluation model of P2 P online lending platform based on big data analysis is proposed in this studey.Based on traditional credit indexes,the quality and the caliber of users are added to the the assessment to get a more comprehensive evaluation model.The method of data mining has been used to integrate more comprehensive user information and valuable indexes can be screened through the information divergence.Based on this model,an example has been performed to verify the feasibility and effectiveness of the risk evaluation model in this study.As for the second problem,that is,the objectivity and the underlying correlation of the traditional evaluation model,a risk control analysis model on the fundation of the research of big data is proposed based on deep learning technology.Using the time sequence to organize data,a deep learning credit evaluation model has been constructed on the basis of the convolutional neural network algorithm.In order to verify the performance of this model,the training-optimized model is compared with logistic regression model,decision tree model and random forest model,using ROC curves and AUC values as evaluation indicators.The results show that the deep learning model possesses higher accuracy,which means that the deep learning model has obvious advantages when analyzing complex nonlinearity subjects with multiple feature.