  At the core of insurance business lies classification between risky and
non-risky insureds, actuarial fairness meaning that risky insureds should
contribute more and pay a higher premium than non-risky or less-risky ones.
Actuaries, therefore, use econometric or machine learning techniques to
classify, but the distinction between a fair actuarial classification and
"discrimination" is subtle. For this reason, there is a growing interest about
fairness and discrimination in the actuarial community Lindholm, Richman,
Tsanakas, and Wuthrich (2022). Presumably, non-sensitive characteristics can
serve as substitutes or proxies for protected attributes. For example, the
color and model of a car, combined with the driver's occupation, may lead to an
undesirable gender bias in the prediction of car insurance prices.
Surprisingly, we will show that debiasing the predictor alone may be
insufficient to maintain adequate accuracy (1). Indeed, the traditional pricing
model is currently built in a two-stage structure that considers many
potentially biased components such as car or geographic risks. We will show
that this traditional structure has significant limitations in achieving
fairness. For this reason, we have developed a novel pricing model approach.
Recently some approaches have Blier-Wong, Cossette, Lamontagne, and Marceau
(2021); Wuthrich and Merz (2021) shown the value of autoencoders in pricing. In
this paper, we will show that (2) this can be generalized to multiple pricing
factors (geographic, car type), (3) it perfectly adapted for a fairness context
(since it allows to debias the set of pricing components): We extend this main
idea to a general framework in which a single whole pricing model is trained by
generating the geographic and car pricing components needed to predict the pure
premium while mitigating the unwanted bias according to the desired metric.
