基于大数据的基本医疗保险参保人欺诈风险评估

目的:构造基本医疗保险参保人欺诈风险预测模型,发现欺诈行为的主要特征,进而建立风险评估指标体系,以期为医保基金智能监管提供决策支持。方法:利用183万多条我国基本医疗保险诊疗历史记录的大规模真实数据,应用XGBoost算法和EasyEnsemble方法构造基本医疗保险参保人欺诈风险评估集成模型。在此基础上,利用特征重要度计算进一步识别和量化欺诈行为人的潜在特征以构造欺诈风险评估指标体系。结果:模型预测结果的准确性为83%;阳性与阴性预测值的加权平均值为95%;参保人欺诈的可能性能够被正确评估的概率为85%;其中,实际产生欺诈行为的所有参保人中,有82%的人员能通过本模型正确识别;各项费用发生金额、各阶段费用发生金额以及各类项目的数量等是区分欺诈与正常参保人的重要指标。结论:基于XGBoost集成模型构建的基本医疗保险参保人欺作风险评估指标体系能够有效地用于识别潜在欺诈人员。建立健全的风险评估指标体系并开发基于医保大数据的智能化监控系统,对于提高医保管理服务水平,保障医保基金安全以及维护社会医保的公平性有重要作用。

Assessment on insurance fraud risk in basic medical insurance in the context of big data

Objectives:To construct a fraud risk prediction model for basic medical insurance holders, discover the main characteristics of fraud, and then establish a risk assessment index system to provide decision support for an apposite supervision of medical insurance funds. Methods:Using the large-scale real data including more than 183 million records of basic medical insurance diagnosis and treatment in China, the integrated risk assessment model for basic medical insurance holders is constructed using XGBoost algorithm and EasyEnsemble method. On this basis, this paper further identifies and quantifies the potential characteristics of fraud enforcement, and thus constructs a fraud risk assessment index system. Results:The proposed integrated model predicted the fraud risk with the accuracy of 83%, balance predictive value of 95%, and the balance sensitivity was 85%, respectively. Most importantly, the probability of the insured fraud being correctly evaluated was 82% in this fraud risk assessment model. Besides, the amount of various expenses incurred at each stage of assessment, and the number of various types of projects are important indicators to distinguish the fraud from the normal insurance holders. Conclusions:The fraud risk assessment index system constructed based on the XGBoost integrated model is effective for the identification of potential fraudsters among the basic medical insurance holders. Establishing a risk assessment index system and developing an apposite supervision system based on big data of medical insurance play an essential role in improving the level of medical insurance management services, which ensures the safety of medical insurance funds, and safeguards the social health insurance fairness.