Consumer Watchdog has expressed concerns over the newly proposed rules by Insurance Commissioner Ricardo Lara regarding the use of catastrophe modeling in the insurance industry.
The organization criticized the proposal for potentially maintaining the secrecy of “black box” models, which it deems inconsistent and unreliable. Consumer Watchdog highlights the necessity for public scrutiny and transparency, particularly before these models are used for rate increases.
As per the watchdog, Commissioner Lara’s proposal may restrict public access to crucial information on how these models impact insurance rates, potentially contravening Proposition 103’s stipulations for transparency.
“The rule fails to spell out whether or how the Department of Insurance would assess a model’s bias, accuracy, or the validity of the science, instead creating a pre-review process that appears primarily focused on determining what information companies must disclose and what they may conceal from public view,” Consumer Watchdog said.
Furthermore, the proposal suggests adopting non-disclosure agreements (NDAs) to satisfy the confidentiality requirements of proprietary model developers. Consumer Watchdog warned that such NDAs could hinder public interest groups from sharing their analysis of these models with the wider public, effectively rendering public participation in the review process futile.
“The rule also proposes expanding the use of catastrophe models far beyond wildfire loss, explicitly expanding them to flood and also allowing the Commissioner, at his discretion, to approve their use in any line of insurance. That could mean auto, non-wildfire residential or commercial, cyber insurance and more. It would also allow insurers to use models to predict all losses, not just catastrophe losses, a dramatic departure from current practice and one that would guarantee an explosion of rates,” the watchdog explained.
In turn, Consumer Watchdog has advocated for the creation of a public catastrophe model to ensure the transparency of climate data and prevent potential price gouging and bias in insurance pricing.
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