Machine learning for the insurance industry: predictive models, fraud detection, and fairness

保险行业的机器学习：预测模型、欺诈检测和公平性

• 批准号: 529584-2018
• 负责人: Marchand, Mario
• 金额: $ 6.33万
• 依托单位: Université Laval
• 依托单位国家: 加拿大
• 项目类别: Collaborative Research and Development Grants
• 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=719617
• 关键词: Machine; learning; insurance; industry; predictive

At the heart of their mission, the insurance industry strives to satisfy their customers and offer them the insurance products that most adequately match their needs. Thanks to a vast amount of corporate data accumulated through the years, to the availability of impressive computational resources, and to the current state of knowledge of machine learning research, insurance companies can now attempt to build effective predictive models about some aspects of client behaviour and their needs. However, insurance companies are also accountable to our society and, in particular, this implies that they should not offer any service and coverage that is, in some way, discriminatory in terms of race, skin colour, ethnic origin, or other irrelevant characteristics that are, arguably, immoral to use. In that sense, the insurance industry should also be fair in the services that they provide. Consequently, this research proposal aims at advancing the current state of knowledge in areas of machine learning research, which are mostly relevant to the insurance industry. More precisely, from the corporate data at SSQ, we aim at building the most accurate, and fair, predictive models for customer needs of insurance products and for some aspects of customer behaviour, such as the likelihood that a client will not renew a given insurance policy. We also aim at building accurate, and fair, fraud detectors with the ability to detect fraud at an early stage and the ability to detect new types of fraud. To meet these objectives, we will need to adapt existing machine learning algorithms in novel ways and design new ones such that they can use and combine different data sources during learning, some of which are sequential in nature. Moreover, we will also need to find ways to enforce fairness into machine learning algorithms such that the predictors output by them will not be using irrelevant sensible attributes (such as race, ethnic origin, religion, etc.) in a way that makes them perform unevenly across different groups of individuals.

保险业的核心使命是努力满足客户的需求，并为他们提供最适合他们需求的保险产品。得益于多年来积累的大量企业数据、令人印象深刻的计算资源的可用性以及机器学习研究的知识现状，保险公司现在可以尝试针对客户行为和行为的某些方面建立有效的预测模型。他们的需求。然而，保险公司也对我们的社会负责，特别是，这意味着他们不应提供任何在种族、肤色、民族血统或其他不相关特征方面具有歧视性的服务和保险。可以说，使用它们是不道德的。从这个意义上说，保险业提供的服务也应该是公平的。因此，本研究提案旨在推进机器学习研究领域的知识现状，这些领域主要与保险业相关。更准确地说，根据 SSQ 的企业数据，我们的目标是针对客户对保险产品的需求以及客户行为的某些方面（例如客户不会续保给定保险的可能性）建立最准确、公平的预测模型政策。我们还致力于构建准确、公平的欺诈检测器，使其能够在早期阶段检测欺诈并能够检测新型欺诈。为了实现这些目标，我们需要以新颖的方式调整现有的机器学习算法并设计新的算法，以便它们可以在学习过程中使用和组合不同的数据源，其中一些数据源本质上是连续的。此外，我们还需要找到方法来强制机器学习算法的公平性，这样它们输出的预测器就不会使用不相关的合理属性（例如种族、族裔、宗教等），从而使它们执行不同群体的个体之间的差异不均匀。