HMN 2026: How Framework could deliver fairer insurance deals for customers

Insurance companies could use a new research-based tool to reduce “proxy discrimination” in the pricing models that shape premiums in the sector.

Proxy discrimination happens when an algorithmic pricing model indirectly infers characteristics—such as ethnicity and sex—from other information provided by potential customers. For example, occupation can act as a proxy for sex, and postcode for ethnicity.

Researchers at Bayes Business School, which is part of City St George’s, University of London, developed a framework that measures the extent of such bias. The tool also identifies the variables that contribute most to proxy discrimination, along with those which sometimes actually reduce it.

Co-author Professor Andreas Tsanakas, Professor of Risk Management at Bayes Business School, says the sector could adopt the framework, which has been published in the European Journal of Operational Research, as it is applicable to most types of insurance cover. It could also be used to identify proxy discrimination in other financial services—such as credit scoring.

He said, “If they choose to, insurance companies could reduce indirect discrimination by using this framework. It could also be a useful diagnostic tool for auditors and regulators. It’s up to regulators to set out clear principles and incentives for insurers to act on the issue.”

Identifying the presence of proxy discrimination requires data on the policyholders’ protected characteristics, only some of which are collected. While potential customers are often asked their sex, for example, data about individual policyholders’ ethnicity are generally not collected. Earlier work by Professor Tsanakas and his co-authors has gone some way to addressing those challenges.

The paper also revealed that some variables can actually reduce proxy discrimination—suggesting, Professor Tsanakas said, that the interplay between pricing factors and fairness is even more complex than previously recognized.

A minor impact of proxy discrimination at a portfolio level, the paper found, can mask significant impact on specific demographic groups. For example, when the researchers introduced policyholder-specific measures to a real-world motor insurance pricing model, they found young drivers from one ethnic group were systematically quoted higher premia. That variation was partially attributed to proxy effects.

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