Selection bias in insurance: why portfolio-specific fairness fails to extend market-wide
Abstract: Fairness centres on people. In insurance, the scope of fairness should be the entire insured population, not solely an insurer's clients. However, each insurance company’s portfolio represents a possibly skewed subsample. We examine how portfolio composition affects fair premium methodologies for mitigating direct and indirect discrimination on a protected attribute. We illustrate how unfairness mitigation based on a selection-biased portfolio does not yield a fair market from the perspective of insureds. Relying on causal inference and a portfolio composition indicator, we describe the selection mechanism and determine conditions under which each bias affects various fairness-adjusted premiums. We propose a method to recover the population-wide fairness-adjusted premiums from selection-biased data, by using a (third-party provided) unbiased estimate of the prohibited attribute distribution. We show that this approach effectively mitigates selection bias but leads to overall premiums that are not balanced.
Seminar organized by Professor HJ. Albrecher