A Scalable Toolbox For Exposing Indirect Discrimination In Insurance Rates

Our paper, ‘A Scalable toolbox for exposing indirect discrimination in insurance rates‘ with Olivier Côté and Marie-Pier Côté, is out.

Here is Alyssa Gambone  (Reserving Actuary at Riverstone International US)’s post on Linkedin

It’s time for one of my rare insurance related posts, though this one isn’t entirely off theme of my normal content. The CAS recently published a paper entitled ‘A Scalable toolbox for exposing indirect discrimination in insurance rates‘ by Olivier Côté, Marie-Pier Côté, and Arthur Charpentier that makes an incredibly important point as the actuarial profession goes deeper and deeper into machine learning and AI.  “In an unrealistic extreme, oracle insurers — capable of perfectly predicting both amount and timing of insurance claims — might charge each policyholder precisely their discounted future claim amount, questioning the very concept of insurance risk transfer.” I’m a big believer that in service of the “most accurate” rates (what the paper calls “actuarial fairness”, which is incredibly damning of our profession), we have lost our purpose, which is to ensure a wide ranging ability of society to take normal risks like driving a car, owning a home, or starting a business. Society is better when insurance is available and affordable, not when it is precisely accurate for the smallest groups possible. In service of the capitalistic goal of maximizing profits at all costs, we have found out that the costs might be our industry’s societal purpose and reason to exist. “As data granularity increases, so does the potential for actuarial justification in perpetuating [historic and socioeconomic] disparities.” Shame on our profession if it does.

There will be much more work published soon on those topics… Meanwhile, here was our abstract,