Abstract: Auto insurance fraud detection plays an important role in promoting the healthy development of auto insurance.As the judgment of fraud involves the core content of civil rights,it is necessary for auto insurance experts to check the case and provide the reasons for fraud.Although the method based on machine learning have strongscalability and high accuracy,it lacks interpre-tability,while the rule method based on expert system has good interpretability,but it is limited by the trigger conditions of complex rules.To address the unexplainable problem of cases detected as“fraud” by machine learning methods without triggering the expert system fraud rules,this paper puts forward an analysis method of auto insurance fraud traceability based on network representationlear-ning.It first defines the abductive analysis task of auto insurance fraud.That is,for cases that are identified as “fraud ”ones by machine learning methods without triggering the expert system,it returns the ranking of the most likely fraud rules to auto insurance experts.Then,the method models the case-rule factor network based on the network representation lear-ning according to the fraud cases that have triggered the rules of the expert system,and learns the vector representation of these factors in fraud rules.To better measure the similarity between fraud cases and rules with incomplete triggering factors in the expert system,a weighted splicing strategy of factors in fraud rules is designed based on the principle of abductive reasoning,which can alleviate the problem of insufficient training data to some extent.Experimental results show that the proposed method can obtain better performances than existing methods in terms of three metrics.

Key words: Auto insurance fraud, Network representation learning, Abductive reasoning, Expert system, Interpretability