Auto Insurance Fraud Detection with Multimodal Learning  

作　　者：Jiaxi Yang Kui Chen Kai Ding Chongning Na Meng Wang 

机构地区：[1]Financial Technological Research Center,Zhejiang Lab,Hangzhou 361005,China [2]School of Computer Science and Engineering,Southeast University,Nanjing 211189,China

出　　处：《Data Intelligence》2023年第2期388-412,共25页数据智能（英文）

基　　金：supported by"Research on intelligent Computing technology in Financial Risk Control and Anti-fraud",funding code 2020NFACO1,Zhejiang Lab,leaded by Dr.Chongning Na.

摘　　要：In recent years,feature engineering-based machine learning models have made significant progress in auto insurance fraud detection.However,most models or systems focused only on structural data and did not utilize multi-modal data to improve fraud detection efficiency.To solve this problem,we adapt both natural language processing and computer vision techniques to our knowledge-based algorithm and construct an Auto Insurance Multi-modal Learning(AIML)framework.We then apply AIML to detect fraud behavior in auto insurance cases with data from real scenarios and conduct experiments to examine the improvement in model performance with multi-modal data compared to baseline model with structural data only.A selfdesigned Semi-Auto Feature Engineer(SAFE)algorithm to process auto insurance data and a visual data processing framework are embedded within AIML.Results show that AIML substantially improves the model performance in detecting fraud behavior compared to models that only use structural data.

关 键 词：Auto Insurance Multi-modal Learning Fraud detection Ensemble learning 

分 类 号：F426.471[经济管理—产业经济] TP181[自动化与计算机技术—控制理论与控制工程] F842.634[自动化与计算机技术—控制科学与工程]