Regulator looking into the ‘black box’ of auto rating models

Auto insurance rating models are becoming so complex that Ontario’s regulator is looking not only at the inputs of these models, but now the outputs as well, attendees heard at an industry event last week.

“There are concerns that these systems will be so complex over time that we don’t understand them,” said Carole Piovesan, co-founder of data law and consulting firms INQ Law and INQ Consulting. “And we’re putting in place certain mechanisms to try to avoid that, including a whole new market around creating AI systems to assess AI systems, to explain those systems.”

The complexity of these rating models, exacerbated by artificial intelligence (AI), has caught the attention of the Financial Services Regulatory Authority of Ontario (FSRA), which is looking to examine not only the inputs of these models but the outputs as well.

Traditionally, regulators looked at the inputs — what could and could not be used to set a rate, Brian Sullivan, editor and owner at Risk Information Inc., said during the 2023 FSRA Exchange event on Jan. 19.

And if certain rating factors could not be used, Sullivan explained, an insurer’s hypothetical response could be: “‘Well, I can just bring this big pile of data over here and accomplish the same thing.” Added Sullivan: “Should we ignore the inputs and instead spend most of our regulatory time examining the outcomes, the outputs, of those systems?”

Tim Bzowey, FSRA’s executive vice president of auto/insurance products, acknowledged FSRA has been very focused as a rate regulation regulator around rating inputs — “effectively, what goes in the soup and maybe not so much how it tastes.”

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Although the regulator meets the statutory standard of rates that are just, reasonable and not excessive, that’s different than having a focus on consumer outcomes, Bzowey said. “If I’m interested

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