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
Based on the structure of chute - feed and autoleveHer, an analysis of their working principle and the verification of their practical production results have been carried out. Finally, the future investigation direet...Based on the structure of chute - feed and autoleveHer, an analysis of their working principle and the verification of their practical production results have been carried out. Finally, the future investigation direetiom of chute - feed and card autuleveller are put forward.展开更多
UBI车险(Usage Based Insurance)是基于车主驾驶行为以及车辆使用相关数据相结合的可量化保险,在欧美等国家已发展10多年,有的国家已逐渐取代传统车险成为主流车险。近年来,随着国内车联网大数据等技术的发展和车险费改的推动,各大公司...UBI车险(Usage Based Insurance)是基于车主驾驶行为以及车辆使用相关数据相结合的可量化保险,在欧美等国家已发展10多年,有的国家已逐渐取代传统车险成为主流车险。近年来,随着国内车联网大数据等技术的发展和车险费改的推动,各大公司也开始发展UBI车险,但发展较为缓慢。中瑞公司作为国内车联网技术和服务的领军企业,与太平洋保险公司合作开发了商用车UBI车险产品,为我国商用车UBI车险提供技术解决方案。分析中瑞公司商用车UBI车险营销策略现状,探讨其存在的不足,提出改进措施。展开更多
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
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.
文摘Based on the structure of chute - feed and autoleveHer, an analysis of their working principle and the verification of their practical production results have been carried out. Finally, the future investigation direetiom of chute - feed and card autuleveller are put forward.
文摘UBI车险(Usage Based Insurance)是基于车主驾驶行为以及车辆使用相关数据相结合的可量化保险,在欧美等国家已发展10多年,有的国家已逐渐取代传统车险成为主流车险。近年来,随着国内车联网大数据等技术的发展和车险费改的推动,各大公司也开始发展UBI车险,但发展较为缓慢。中瑞公司作为国内车联网技术和服务的领军企业,与太平洋保险公司合作开发了商用车UBI车险产品,为我国商用车UBI车险提供技术解决方案。分析中瑞公司商用车UBI车险营销策略现状,探讨其存在的不足,提出改进措施。
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.
