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: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=658725
• 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.**********************

在他们的任务中，保险业致力于满足客户的满足，并为他们提供最适合其需求的保险产品。多年来积累的大量公司数据，令人印象深刻的计算资源以及机器学习研究的当前知识状态，保险公司现在可以尝试建立有关客户行为及其需求的某些方面的有效预测模型。但是，保险公司也对我们的社会负责，尤其是这意味着他们不应在某种程度上提供任何在种族，肤色，种族血统或其他无关的特征方面具有歧视性的服务和覆盖范围，这些特征可以说是不道德使用的。从这个意义上讲，保险业也应该在其提供的服务中公平。 ****因此，该研究建议旨在推进机器学习研究领域的当前知识状态，这些领域与保险行业大多相关。更确切地说，从SSQ的公司数据来看，我们旨在为保险产品的客户需求以及客户行为的某些方面建立最准确，最公平的预测模型，例如客户不会续订给定的保险单。我们还旨在建立准确且公平的欺诈探测器，能够在早期发现欺诈和检测新型欺诈的能力。为了实现这些目标，我们将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并结合不同的数据源，其中一些本质上是顺序的。此外，我们还需要找到方法来在机器学习算法中实施公平性，以使他们的预测变量不会使用不相关的明智属性（例如种族，种族，宗教，宗教等），以使它们在不同的个人群体中执行不均匀。