We consider the following question: given information on individual policyholder characteristics, how can we ensure that insurance prices do not discriminate with respect to protected characteristics, such as gender? We address the issues of direct and indirect discrimination, the latter meaning that we can learn protected characteristics from non-protected ones. We provide rigorous mathematical definitions for direct and indirect discrimination, and we introduce a simple formula for discrimination-free pricing, that avoids both direct and indirect discrimination. Our formula works in any statistical model. We demonstrate its application on a health insurance example, using a state-of-the-art generalized linear model and a neural network regression model. An important conclusion is that discrimination-free pricing in general requires collection of policyholders’ discriminatory characteristics, posing potential challenges in relation to policyholder’s privacy concerns.
– An overview and definition of discrimination in insurance pricing will be provided
– Methods for removing unwanted discrimination from pricing models will be discussed an illustrated
– Participants will come away with a deeper appreciation for the factors at play when considering discrimination in insurance pricing
– In addition, machine learning methods will be demonstrated in the presentation and paper.