New algorithms and new data for insurance : impact of machine learning techniques in insurance ratemaking

保险新算法和新数据：机器学习技术对保险费率制定的影响

• 批准号: RGPIN-2019-07077
• 负责人: Charpentier, Arthur
• 金额: $ 1.46万
• 依托单位: Université du Québec à Montréal
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=739297
• 关键词: New; algorithms; new; data; insurance

Actuarial valuation of contracts for setting premium levels that insurance companies must ask, is based on 'ratemaking variables' intended to properly reflect the risk of each insured. For example, the frequency of claims for the young drivers in motor insurance is (on average) twice as high as for their parents. So insurers have long used the age of the driver as a rating variable. The presence of new data sources will help to refine profiling insured and thus the assessment of their own risk. The classic use of GLM-type models must be adapted to incorporate this large amount of data. Examples ongoing work on the inclusion of data related to driving experience, obtained through GPS boxes in motor insurance. One of the research areas of this proposal will be to develop and implement innovative statistical methods to better understand the proper actuarial risk for each insured. In addition to classical economic considerations, actuaries must consider statistical aspects related to the typology of the insured sample available. In particular, in a competitive environment, the data used by actuaries to value the contracts should reflect selection bias. Hence, if an insurer conducts high tariff policy to attract certain types of risk, they will go to competitors, and these insurance companies will then have a very small sample to value these contracts. Hyper-segmentation will create very heterogeneous portfolios, with some categories little present in some companies. Conventionally, the difficulty lies in estimating the moral hazard of each insured while eliminating bias due to adverse selection. Currently the calculation of premiums and insurance premiums is simply using claims data observed in previous years, usually using standard econometric models. In a competitive environment, should be integrated competitors' prices as an explanatory variable in the decision to purchase an insurance policy. We find ourselves in a situation where econometric models are in competition and are influenced one. Price modeling on a market must be done using tools theory of non-cooperative games, and econometric models. One of the goal of this research program will also focus on the integration of competitors price in ratemaking. Actuarial valuation is based on the law of large numbers and the pooling of risks among the insured. Companies try to establish risk mutual societies, grouping all insured with common characteristics. As usually said, insurance is 'the contribution of the many to the misfortune of the few'. But is this concept of risk pooling compatible with the principles of segmentation and (hyper-)individualization of insurance contracts? One of the goal of this research proposal is to understand the impacts in terms of pricing of the necessary the balance between segmentation of policyholders and risk pooling.

保险公司必须对设定保险费水平的合同进行精算估值，其基础是“费率制定变量”，旨在正确反映每个被保险人的风险。例如，在汽车保险中，年轻司机的索赔频率（平均）是其父母的两倍。因此，保险公司长期以来一直将司机的年龄作为评级变量。新的数据来源的出现将有助于完善投保人的特征分析，从而评估他们自己的风险。GLM型模型的经典使用必须加以调整，以纳入这一大量的数据。举例说明正在进行的关于将通过全球定位系统箱获得的驾驶经验数据纳入汽车保险的工作。该提案的研究领域之一将是开发和实施创新的统计方法，以更好地了解每个被保险人的适当精算风险。除了传统的经济考虑之外，精算师还必须考虑与现有投保样本类型有关的统计方面。特别是，在竞争环境中，精算师用来对合同进行估值的数据应反映选择偏差。因此，如果一家保险公司为吸引某些类型的风险而采取高费率政策，他们就会转向竞争对手，而这些保险公司将只有非常小的样本来评估这些合同。超细分将创建非常异构的投资组合，某些类别在某些公司中几乎不存在。传统上，困难在于估计每个被保险人的道德风险，同时消除由于逆向选择的偏见。目前，保费和保险费的计算只是使用前几年观察到的索赔数据，通常使用标准的计量经济模型。在竞争环境中，应将竞争者的价格作为解释变量综合考虑，以决定是否购买保险。我们发现自己处于这样一种情况，即计量经济学模型处于竞争之中，并受到竞争的影响。在市场上的价格建模必须使用非合作博弈的工具理论和计量经济模型。本研究计划的目标之一也将集中在竞争对手的价格在费率制定的整合。精算估值是基于大数定律和投保人之间的风险分担。公司试图建立风险互助协会，将所有具有共同特征的保险人分组。正如通常所说，保险是“许多人对少数人的不幸的贡献”。但这种风险统筹的概念是否与保险合同的细分和（超）个性化原则兼容？本研究建议的目标之一是了解在定价方面的影响，必要的投保人和风险池之间的分割平衡。