Anti-Discrimination Insurance Pricing: Regulations, Fairness Criteria, and Models

Abstract

On the issue of insurance discrimination, a grey area in regulation has resulted from the growing use of big data analytics by insurance companies – direct discrimination is prohibited, but indirect discrimination using proxies or more complex and opaque algorithms can be tolerated without restrictions. Meanwhile, various fairness criteria have been proposed and flourish in the machine learning literature with the rapid growth of artificial intelligence (AI) in the past decade, which generally focus on a classification decision. However, there is little research on insurance applications, particularly on insurance pricing as a regression problem. In this paper, we summarise the fairness criteria that are potentially applicable to insurance pricing, match them with different levels of anti-discrimination regulations, and implement them into a series of existing and newly proposed anti-discrimination insurance pricing models. Our empirical analysis compares the outcome of different models and shows the potential of indirect discrimination.