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.

根据保险公司必须提出的设定保费水平的合同的精算估值，是基于“比例制定变量”，旨在正确反映每个被保险人的风险。例如，年轻驾驶员在汽车保险中的索赔频率（平均）是父母的两倍。因此，保险公司长期以来将驾驶员的年龄用作评级变量。新的数据源的存在将有助于改善分析的保险，从而评估其自身风险。 GLM型模型的经典用途必须适应以包含大量数据。示例正在进行的有关包含与驾驶经验有关的数据的工作，该数据是通过汽车保险中的GPS盒获得的。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。除经典的经济考虑外，精算师还必须考虑与可用样本的类型学相关的统计方面。特别是，在竞争环境中，精算师用来重视合同的数据应反映选择偏见。因此，如果保险公司执行高关税政策以吸引某些类型的风险，则将转移给竞争对手，这些保险公司将有一个很小的样本来重视这些合同。超级细分会产生非常异构的投资组合，某些公司中的某些类别很少。从传统上讲，困难在于估计每个被保险人的道德危害，同时由于不良选择而消除了偏见。目前，保费和保险费的计算仅使用前几年观察到的索赔数据，通常使用标准计量经济学模型。在竞争环境中，应将竞争对手的价格纳入决定，以作为购买保险单的决定的解释变量。我们发现自己处于计量经济学模型正在竞争并受到影响的情况下。必须使用非合作游戏和计量经济学模型的工具理论进行市场上的价格建模。该研究计划的目标之一还将集中于竞争对手价格在比例制定中的整合。精算估值基于大量定律和被保险人之间的风险集合。公司试图建立风险共同社会，将所有人都具有共同特征分组。如通常所说，保险是“许多人对少数人的不幸的贡献”。但是，这种风险汇集的概念是否符合分割和（超级）个性化合同的原则？这项研究建议的目标之一是了解必要的对保单持有人分割和风险汇集之间的平衡的影响。