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

点击翻译按钮获取中文摘要

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.