Research On Frequency Prediction Of Auto Insurance Claims Based On Deep Neural Network Model

Machine learning is one of the research hotspots in the field of big data analysis and processing in recent years.This method has been used successfully in many fields,but it is seldom used in auto insurance claim prediction.In recent years,the generalized linear model has been widely used in auto insurance market pricing.With the constant improvement of the level of competition at home and abroad,high-precision car insurance products pricing is the core of the majority of domestic and foreign mature insurance market,this forced insurance company constantly optimize the structure of evaluation standards and prices,and more and more important is to consider the customer behavior characteristic information,such as the customer's driving experience,age,car brand,use fixed number of year,etc.,and even customer driving habits,psychological character,precise pricing according to the characteristics of information further.When these more and more characteristic factors are taken into account in the traditional actuarial model framework based on generalized linear family of Models(GLMs)and generalized additive family of models(GAMs),the model precision becomes more and more inadequate,and there is often a large deviation loss.Some research results show that machine learning is superior to the generalized linear model in the processing of characteristic variables.By modeling and calibrating the data using the neural network method in machine learning,the deviation loss can be reduced well,the claim risk of customers can be better reflected,and the auto insurance pricing can be further carried out more accurately.However,these results are only based on a single method,which cannot systematically improve the classical generalized linear model and generalized additive model.As an important part of machine learning,neural network algorithm in the regression model in the process of data modeling prediction and classification has significant advantages,when the variables of the model was gradually increased,compared with the shallow depth of neural network neural network further also reflects the advantage,therefore,this research in the classic modeling and shallow neural network model on the basis of further explore depth of neural network model for the influence of model prediction accuracy.On the one hand,by reviewing the theoretical research and practical application of neural network algorithm model in auto insurance claim frequency at home and abroad,the application scope of neural network under different structures is mastered,and the general idea of neural network model to solve regression problem is obtained.Comb at home and abroad,on the other hand,problems about neural network is applied in car insurance claims frequency of related research,neural network model and the merits and demerits of the traditional generalized linear model,using the embedded model combines neural network model and the traditional generalized linear model,the depth of neural network algorithm is put forward improvement methods to the problems of claim frequency.