Risk Assessment And Loan Demand Forecasting Of Unsecured Loan

With the continuous improvement of people's living standards,the consumer finance industry has emerged and there is a stronger demand for unsecured loans.This gives financial institutions development opportunities and huge profits.Many banks and Internet finance companies have launched their own unsecured loan products to meet the needs of society for funding.However,at the same time,it also brings huge risks to financial institutions.The risks mainly come from two aspects.On the one hand,customers apply for unsecured loans through the Internet,and financial institutions cannot verify the authenticity of users' information.Therefore,many customers use fraudulent information or use the Internet technology to swindle the loan.On the other hand,customers pay a low price for unsecured loan defaults,which leads to a large number of defaults in unsecured loans.Once these two risks occur,they may cause huge losses,which are difficult to make up,to financial institutions and may even cause financial institutions to close down.In order to ensure the normal operation of financial institutions,it is necessary to conduct a more accurate assessment of the risk of unsecured loans to customers,to predict the possibility of customer default,and provide a basis for decision-making whether financial institutions should lend money to the customer.At the same time,in order to maximize the profits of financial institutions,it is necessary to forecast the total amount of customer loans and optimize the rational allocation of funds.With the continuous development of big data technology,big data risk control has achieved remarkable results.Under this background,this paper mainly studies the application of big data technology in unsecured loan business.The risk assessment model was developed by stacking Logistic Regression,Random Forests,and Extreme Gradient Boosting model to predict the probability of customer defaults and reduce the risk of financial institutions.Then use Extreme Gradient Boosting model to model a forecast model to forecast the loan demand of customers,which can improve the financial institution's utilization of funds.And provide financial institutions with funding distribution basis.In order to verify the feasibility and validity of the model proposed by this paper,an unsecured loan business data from a financial institution in China was used for empirical analysis.The analysis showed that the accuracy of the risk assessment model for predicting breach of contract reached 95%.The accuracy of this model is better than any one of Logic Regression,Random Forests,and Extreme Gradient Boosting.The square root of the error of loan demand forecasting model is 0.105,indicating that the model error was small and the prediction accuracy of prediction model was high.The results of empirical analysis show that the risk assessment model and forecasting model established in this paper are feasible and effective,and have broad application prospects.