Study On Online P2P Lending Investment Risk Evaluation Based On Genetic Neural Network And Game Theory

Online P2 P lending is increasingly prosperous.More and more investors choose P2 P platform to invest and financing.Although with high returns,online lending also has higher investment risks than traditional investment.With the rapid development of the P2 P industry,a lot of platform closed down.A variety of risk problems continue to appear.At the same time,in the case of serious asymmetric information,many investors can not do a good assessment of investment risk and make scientific and rational investment decisions.I have read a lot of literature about online P2 P lending industry at home and abroad.By summarizing the theories and methods of China's online P2 P lending investment risk and analyzing the causes of the risk of domestic online P2 P lending,key factors in determining the risk of P2 P platform were found out and the P2 P platform risk assessment index system was built.To solve the defects of the theory and application of BP neural network,genetic algorithm is used to optimize the BP neural network,then the genetic neural network model is established to evaluate the risk of the P2 P platform.At the same time,we use the MATLAB tool to realize the optimization of BP neural network by genetic algorithm,in which the training sample data comes from the net loan home,Jia Lu data and other third party platform.After the risk assessment is completed for the P2 P platform,an incomplete information game theory model of P2 P investors and borrowers has been set up.The borrowers has been divided into high risk borrowers and low risk borrowers.By building two different risk borrowers' game theory matrix,we can discuss the effect of information asymmetry on both lending behavior decision.It is critical to establish scientific and reasonable P2 P investment risk evaluation model to help investors to better evaluate the project risk.The risk evaluation index system established in this paper takes into account many factors,which combines the traditional commercial banks and the risk factors that reflect the characteristics of online P2 P loan.Secondly,by analyzing the results of genetic neural network model,we can find that this model can effectively evaluate the risk of the P2 P platform,and to recognize normal platform and sensitive problem.The accurate rate is far greater than the simple use of BP neural network platform for risk assessment,which verify the model's feasibility and applicability.Finally,through the analysis of game theory,information asymmetry will influence the behavior of investors and borrowers,so investors should not blindly pursue high returns,but to diversify the choice of the project,to reduce the potential loss.