The Application Of XGBOOST In Vehicle Insurance Fraud Identification

With our country economic development,the insurance market scale continues to grow,especially in vehicle insurance industty to flourish.But in the hidden behind in the development of many problems,such as frequent car insurance fraud,the continued growth of the amount in industry as a whole compensation expenditure on the high side,indirectly led to the insured premiums increased,it has a bad influence on both the country and the individual.So how to identify fraud,prevent and stop fraud cases,is one of the key work of each property insurance companies,is the necessary condition to reduce the loss rate.At present,there are two types of vehicle insurance fraud identification:industryspecific actions and internal identification of insurance companies.The former is generally carried out by insurance regulatory authorities,local economic investigation bureaus and insurance companies on an irregular basis,while the latter mainly relies on the ability and experience of the staff of the claims settlement department of each insurance company to identify fraud.Some companies have introduced anti-fraud systems to assist claims adjusters in making decisions and solving cases.Anti-fraud capabilities vary from company to company because claims adjusters have varying levels of skill and experience and are difficult to replicate.At the same time,the existing anti-fraud system in the industry is mainly based on the rule engine to realize the identification of fraud cases,which can not keep up with the changing fraud means and has limited effect,so it has not been widely used.Therefore,it is hoped to improve the fraud identification ability of insurance companies with the help of machine learning technology,which is also in line with the development trend of insurance companies’information system construction toward big data and artificial intelligence in recent years.Based on the internal data of insurance companies,this paper refers to the industry experience and relevant literature research results,corrects some errors,establishes the connection between data and business experience,and finally uses decision tree,GBDT and XGBoost respectively to model and verify the effect,which proves that the identification effect can be achieved through the internal data modeling of insurance companies.In addition,the role of machine learning in fraud identification can be further improved by reforming existing systems or connecting with third-party data to enrich data sources and content.