Research On The Application Of Artificial Intelligence In Insurance Anti-fraud

Insurance fraud is a real problem faced by the insurance industry,which seriously disrupts the order of the insurance market.Risks exist in all aspects of people's life and work,which determines that the business scope of insurance companies needs to cover all aspects,and it has a wide range of business objects.However,because insurance companies cannot fully obtain relevant information about policyholders,the insurers are in a relatively weak position in the process of formulating insurance policies,resulting in increasingly frequent insurance fraud activities.In order to conduct insurance anti-fraud,the insurance academia has made many different attempts to identify insurance anti-fraud behaviors,such as using PRIDIT model,Probit model,expert rule system,etc.,but similar methods can only deal with relatively low-dimensional data,When dealing with large-scale and high-dimensional data,the ability is limited.With the rapid development of information technology and the wide application of artificial intelligence,insurance data will also show a multi-dimensional development trend,but neither manual identification nor Logistic regression,SVM,ELM and other technologies can identify and process such emerging data.Therefore,the insurance industry is imminent for new technologies,and artificial intelligence technology is a very suitable choice.Therefore,this paper focuses on the role of artificial intelligence in the process of insurance anti-fraud.This paper uses the insurance claims data of insurance companies in the past,and uses big data analysis,machine learning models and deep learning models to identify real fraud event data,so as to study the role of artificial intelligence technology in anti-fraud of insurance companies.In this paper,by training different models and comparing the prediction accuracy of each model after training,we find the model with the highest identification ability of insurance anti-fraud,and then further optimize the model based on the model to obtain the best insurance anti-fraud recognition ability.Excellent model.Finally,the corresponding countermeasures and suggestions are put forward according to the prediction results of the optimal model.The main conclusions of this paper are as follows:First,in the horizontal comparison of models,the accuracy rate indicators of the baseline model under the condition of setting the adjustment rate are better than other model indicators in this group.The performance of various deep learning models is basically the same,and the DCN model is one of the representative models.Second,in the longitudinal comparison of models,the final model's accuracy index under the condition of setting the adjustment rate is better than that of the baseline model.Compared with the baseline model,the prediction ability of the intermediate model is lower,but as an intermediate link,its prediction ability is higher than that of the baseline model.Strength is not a strong reference.Third,the final model far outperforms the baseline model on the upscaling rate metric when the accuracy benchmark is set.The performance of the single data test response time index of the final model is average,indicating that the model still has the possibility of removing the rough and saving the fine.To sum up,this paper confirms that artificial intelligence can promote insurance antifraud,and builds a model with good insurance anti-fraud identification ability,and successfully finds countermeasures against insurance fraud.Therefore,insurance companies should vigorously improve the level of artificial intelligence,with a view to In the future,machines can be used to replace labor.On the one hand,it can improve its own insurance anti-fraud identification ability,reduce insurance claims fraud rate and insurance company losses,on the other hand,it can also force fraudsters to reduce fraud and help build a good insurance industry ecology.