Abstract

In recent years, multiple global events have drawn society's attention to fairness-related issues and various societal movements resulted from them. For many fields, the impact was immediate and substantial, but for others it has been much more timid. Insurance is one of the latter. More specifically, the way fairness is implemented in algorithms used to calculate insurance premiums has not changed in decades due in part to the lack of modernization from regulators and in part to the complexity of the issue. Nonetheless, in preparation for society's growing expectations, researchers have developed many ways to implement algorithmic fairness. An exposition is made on this concept, including qualitative and quantitative definitions of fairness as well as approaches to its implementation found in the literature. In particular, the method developed by Lindholm et al. (2022) is discussed in detail and followed up by the introduction of our own novel approach. This approach is demonstrated on simulated data, and it is shown that it can significantly reduce unfairness according to pre-determined metrics.