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 mul...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.展开更多
In this paper, the Automated Actuarial Loss Reserving Model is developed and extended using machine learning. The traditional actuarial reserving techniques are no longer compatible with the increase in technological ...In this paper, the Automated Actuarial Loss Reserving Model is developed and extended using machine learning. The traditional actuarial reserving techniques are no longer compatible with the increase in technological advancement currently at hand. As a result, the development of the alternative Artificial Intelligence Based Automated Actuarial Loss Reserving Methodology which captures diverse risk profiles for various policyholders through augmenting the Micro Finance services, Auto Insurance Services and Both Services lines of business on the same platform through the computation of the Comprehensive Automated Actuarial Loss Reserves (CAALR) has been implemented in this paper. The introduction of the four further types of actuarial loss reserves to those existing in the actuarial literature seems to significantly reduce lapse rates, reduce the reinsurance costs as well as expenses and outgo. As a matter of consequence, this helps to bring together a combination of new and existing policyholders in the insurance company. The frequency severity models have been extended in this paper using ten machine learning algorithms which ultimately leads to the derivation of the proposed machine learning-based actuarial loss reserving model which remarkably performed well when compared to the traditional chain ladder actuarial reserving method using simulated data.展开更多
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.
文摘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.
文摘In this paper, the Automated Actuarial Loss Reserving Model is developed and extended using machine learning. The traditional actuarial reserving techniques are no longer compatible with the increase in technological advancement currently at hand. As a result, the development of the alternative Artificial Intelligence Based Automated Actuarial Loss Reserving Methodology which captures diverse risk profiles for various policyholders through augmenting the Micro Finance services, Auto Insurance Services and Both Services lines of business on the same platform through the computation of the Comprehensive Automated Actuarial Loss Reserves (CAALR) has been implemented in this paper. The introduction of the four further types of actuarial loss reserves to those existing in the actuarial literature seems to significantly reduce lapse rates, reduce the reinsurance costs as well as expenses and outgo. As a matter of consequence, this helps to bring together a combination of new and existing policyholders in the insurance company. The frequency severity models have been extended in this paper using ten machine learning algorithms which ultimately leads to the derivation of the proposed machine learning-based actuarial loss reserving model which remarkably performed well when compared to the traditional chain ladder actuarial reserving method using simulated data.
