Assertion Supported AI assessment confidence: 92% certainty 4/5 debate potential 3/5

Boomhauer: Proprietary insurer catastrophe models are impossible for objective third-party evaluation.

Dr. Judson Boomhauer · How climate disasters are shaping insurance markets · Apr 10, 2025 · at 15:55

Environmental economist Dr. Judson Boomhauer discusses the difficulty of verifying catastrophe risk modeling accuracy across the property insurance sector.

0:00 / 0:16exact quote · 16.2s

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“Almost all of the models that are applied by insurers today are developed by for-profit private companies. It makes them all a little bit of a black box. It makes them all basically impossible for objective third parties to evaluate”

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The claim, plainly

Almost all catastrophe models used by insurers are proprietary systems developed by for-profit private companies, making them black boxes that are basically impossible for objective third parties to evaluate.

a quote alone can be ambiguous, so this restates it with the references and context of the conversation around it resolved. AI-written; the tape below the player is the source.

Assessment

Evidence confirms both parts of the claim. First, the catastrophe risk modeling market for property and casualty insurers and reinsurers is overwhelmingly dominated by for-profit commercial vendors (principally Moody's RMS, Verisk Extreme Event Solutions, CoreLogic, and Karen Clark & Company), with open-source and public models accounting for a minimal share of commercial adoption. Second, these commercial models are proprietary systems protected by corporate trade secrets, preventing general independent third parties and researchers from accessing or evaluating their underlying source code, vulnerability functions, and internal mechanics. While specialized state-mandated regulatory bodies (notably the Florida Commission on Hurricane Loss Projection Methodology) conduct confidential on-site reviews for state rate filings, insurance regulatory guidance (e.g., NAIC, Actuarial Standards Board ASOP No. 38) and academic literature widely recognize these systems as proprietary 'black boxes' inaccessible to standard third-party evaluation.

Market data indicates that major commercial vendors (Moody's RMS, Verisk, CoreLogic, KCC) capture the vast majority of P&C insurer catastrophe modeling usage, with 9 of the top 10 commercial lines insurers and 10 of the top 10 reinsurance brokers using RMS alone.
Open-source and public models (such as FEMA HAZUS, the Florida Public Hurricane Loss Model, and Oasis Loss Modelling Framework) represent only a minor fraction of insurer underwriting and ratemaking implementations.
Actuarial Standards Board (ASOP No. 38) and NAIC catastrophe modeling guidelines explicitly address the non-public, proprietary 'black box' nature of commercial vendor models.
Academic evaluations (e.g., Journal of Catastrophe Risk and Resilience, 2026) note that independent public and third-party validation remains restricted due to protected intellectual property and proprietary algorithms.

Checked against

[1] continuuiti.com [2] catmanagers.org [3] apexinsurancebrokers.co.uk [4] htfmarketinsights.com [5] dataintelo.com [6] moodys.com [7] catmanagers.org [8] europa.eu

"Florida Commission on Hurricane Loss Projection Methodology" "RMS" "AIR" "CoreLogic" "Karen Clark" market share catastrophe risk models insurers Moody RMS Verisk KCC catastrophe model market share insurers RMS Verisk CoreLogic KCC catastrophe models commercial vendor market share RMS AIR CoreLogic NAIC "catastrophe modeling" "black box" "proprietary" "catastrophe model" "black box" proprietary "RMS" "AIR" "CoreLogic" market share NAIC catastrophe modeling proprietary vendor market share

assessed as of 2026-09-02

This assessment is an AI reading of a public statement, made against the cited references with the confidence shown. It can be wrong. The quote, the tape and the references are all on this page; if something is off, use report an error below. full disclaimer →

Mentioned

catastrophe models for-profit private companies insurers

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