Insurance Fraud Identification Based On Interactive Dynamic Evaluation Method

With the development of the insurance industry,insurance fraud has become an increasingly serious issue.Insurance fraud poses serious risks to insurance companies,victims,and the entire society,and therefore has been widely recognized and severely cracked down upon.In the insurance industry,there is growing awareness of insurance fraud.Insurance companies prevent insurance fraud by developing preventive measures and strengthening internal management.At the same time,insurance companies also use various technological means and data analysis methods,such as artifcial intelligence and machine learning,to improve their ability to identify and prevent insurance fraud.Common forms of insurance fraud include false claims,short-term insurance fraud,and intentional misrepresentation by insured parties.False claims refer to deliberate actions taken by insurance participants to create or exaggerate accident losses in order to obtain more insurance compensation.Short-term insurance fraud refers to insurance participants who repeatedly purchase insurance in a short period of time and make a large number of claims in a short period of time to obtain illegal benefts.The harm of insurance fraud to society is mainly refected in the following aspects: First,it seriously damages the economic interests of insurance companies and afects their normal operation and development.Second,it disrupts the fair and competitive environment of the insurance market and afects the healthy development of the insurance market.Third,it afects the rights and interests of victims and the fairness and justice of society.Fourth,it wastes social resources and increases social costs.Insurance fraud not only harms the interests of insurance companies,but also afects the fairness and justice of society.Therefore,how to efectively identify insurance fraud has become an important issue that insurance companies need to face and solve.In the United States,insurance fraud is mainly regulated and prosecuted by state and federal governments.The federal government strengthens the crackdown on insurance fraud by enacting laws such as the Insurance Fraud Prevention and Enforcement Act.In China,insurance fraud is also receiving attention and crackdown.The China Insurance Regulatory Commission strengthens the regulation of the insurance market and cracks down on insurance fraud through the enactment of relevant laws and regulations.Currently,insurance companies also use various technological means to prevent and combat insurance fraud.For example,insurance companies use big data and artifcial intelligence technology to establish data analysis models to identify potential insurance fraud.At the same time,they can also enhance their monitoring and prevention capabilities of insurance fraud by establishing an internal reporting system and strengthening business process management.This article introduces a screening algorithm called IDEM(Interactive Dynamic Evaluation Method),which is designed to improve the integration learning efect in the identifcation and classifcation of insurance fraud.Unlike the traditional method of statistical evaluation based on the performance results of all sub-classifers,IDEM adjusts the upper and lower limits of the screening interval one by one according to the introduced value and the distance from the upper and lower limits of the screening interval of each subclassifer,thus achieving an interactive and dynamic evaluation method for sub-classifers that changes with the learning of random forests.This article provides an overview of the defnition,types,and harms of insurance fraud,followed by an introduction to the basic principles and characteristics of the random forest method.The interactive dynamic evaluation(IDEM)method is then introduced,which improves the integration learning efect in the identifcation and classifcation of insurance fraud.Unlike traditional methods that rely on the performance results of all sub-classifers,IDEM adjusts the upper and lower limits of the screening interval based on the introduction of each sub-classifer’s result and the distance from the introduction value to the upper and lower limits of the screening interval,thus achieving an interactive dynamic evaluation method for sub-classifers that changes with the learning of the random forest.The article then describes the modeling process and evaluation method of the improved random forest model.Finally,experimental results are presented to validate the efectiveness of the improved random forest model,which is compared with traditional random forest models and other commonly used classifers in the feld of insurance fraud identifcation,such as logistic regression,k-nearest neighbors,naive Bayes,and support vector machines.The experimental results show that the improved random forest model has not only a higher overall accuracy in the identifcation of insurance fraud but also a signifcant advantage in identifying the minority class,i.e.,fraudulent samples.When using a real dataset,the identifcation accuracy of the improved random forest model can reach more than 78%,while also improving the stability and robustness of the model.Compared with the original random forest and other machine learning algorithms,including logistic regression,k-nearest neighbors,naive Bayes,and support vector machines,the IDEM-improved random forest model has significant classifcation advantages in accuracy,RMSE,precision,and other indicators.The research fndings of this paper have important implications for insurance companies in identifying and preventing insurance fraud.The improved random forest method can efectively enhance the accuracy and stability of insurance fraud detection,providing strong support for insurance companies to prevent and combat insurance fraud.Additionally,the IDEM screening algorithm proposed in this paper can also provide reference and inspiration for other machine learning problems.However,there are still some shortcomings in this research.For instance,the model’s generalization ability and interpretability need further investigation and improvement.Moreover,the model still requires further optimization and enhancement to cope with the constantly changing insurance fraud methods and strategies.Future research can explore more feature selection methods and sampling techniques to improve the performance and efectiveness of insurance fraud identifcation models.