On August 4, 2026, Senator Elizabeth Warren and Representative Ayanna Pressley led twenty members of Congress in writing to USAA, State Farm, Progressive, Liberty Mutual, Farmers and Allstate. The letters demand answers by August 17 on how credit-based insurance scores drive homeowners underwriting and pricing (Pressley press release, August 2026).

The letters reopen the longest-running rating-variable dispute in personal lines, at the moment state AI-governance frameworks have given regulators new language for it.

Key Takeaways

  • Twenty members of Congress, seven senators and thirteen House members, wrote to six carriers on August 4, 2026 demanding responses by August 17 on credit-based insurance scores in homeowners underwriting.
  • The testable claim is that credit moves homeowners premiums as much as disaster risk. That is a statement about relativities, checkable against approved rating manuals already on file.
  • The FTC's 2007 report found both things at once: scores predict claims risk after controlling for other rating variables, and they distribute unevenly across racial and ethnic groups.
  • Removing the variable redistributes premium rather than removing it. The rate credit was carrying migrates to territory, prior claims and home characteristics, while the aggregate need stays anchored to losses.
  • A 10% renewal cap against a 25% uncapped indication carries systematic rate inadequacy that earns through for years, concentrated in the segment most able to shop.

What the Letters Ask, and the Claim That Is Testable

The signatories span both chambers. Seven senators signed, including Warren, Richard Blumenthal, Tammy Duckworth, Ruben Gallego, Adam Schiff, Chris Van Hollen and Ron Wyden, alongside thirteen House members including Pressley, Alexandria Ocasio-Cortez, Ilhan Omar, Rashida Tlaib and Jim McGovern (InsuranceNewsNet, August 2026). The demand is information rather than legislation: how each carrier uses the scores in underwriting and pricing homeowners policies.

Two lines tell actuaries where the argument is headed. "Insurers should not charge consumers higher premiums simply because of their personal credit history," the lawmakers write, framing the variable as causally irrelevant to loss.

The sharper claim is empirical: "Credit scores impact homeowners insurance premiums as much, if not more than, disaster risk in many parts of the country." That comparison, credit weight against catastrophe-peril weight in the rate, is a testable statement about relativities rather than a values argument. It is the one carrier responses will have to engage.

It is also checkable from documents already public. Credit-tier factor tables and territory factor tables both sit in approved rating manuals, and in many filings the worst-to-best credit spread runs well past 2-to-1. The carriers' strongest factual point is that territory factors are not the whole catastrophe load. Hurricane and wind-hail deductibles, roof schedules, by-peril rating and inspection-driven surcharges all carry hazard signal outside the territory table, so a two-column comparison understates the disaster-risk share of the rate.

Predictive Lift, Uneven Distribution, and a New Governance Frame

The evidence record here is deeper than for almost any other rating variable, and it points in two directions simultaneously. The Federal Trade Commission's 2007 report to Congress found credit-based insurance scores to be effective predictors of claims risk under automobile policies, correlating with claim frequency even after controlling for other rating variables (FTC, July 2007). The Texas Department of Insurance reached similar conclusions from carrier data. On the actuarial merits the variable separates loss experience.

The same report documented the distributional fact that keeps the fight alive: scores are not evenly distributed across racial and ethnic groups, with Black and Hispanic consumers overrepresented in the lowest bands. Predictive lift and uneven distribution are both true, which is why the debate does not resolve. Risk classification standards address the first; the political system responds to the second.

The letters aim at homeowners rather than the auto book the FTC studied, and homeowners is the harder line to hold right now, because premiums have already climbed steeply with catastrophe costs.

What differs from the last credit fight is the machinery. More than 20 jurisdictions have adopted the NAIC's model bulletin on insurer AI use. Colorado's quantitative testing regime under SB 21-169 requires life insurers to test models and external data for unfairly discriminatory outcomes, a template its regulators have signaled extends to other lines (NAIC, 2026).

Credit-based insurance scores are exactly the external, non-causal consumer data those frameworks were written for. A carrier running credit through a machine-learning rating plan has converted a familiar scalar into a model feature carrying governance, documentation and outcome-testing expectations that did not exist in 2007. That cuts both ways: it hands critics a governance vocabulary, and it hands carriers the same disparate-impact testing methods, including inferred-demographics analysis of rating outcomes, that work on credit as well as on any novel variable.

What Removing the Variable Would Actually Cost

Several states have already restricted the practice, which supplies natural experiments. California, Hawaii and Massachusetts prohibit the scores in auto insurance, and Michigan, Oregon and Utah restrict them; for homeowners, California, Massachusetts and Maryland bar the practice (Experian, 2026). Washington's commissioner tried an emergency-rule ban in 2021 and was struck down in state court the following year, a reminder that removing a rating variable usually runs through legislatures. Bills are pending in Iowa, New York, Oklahoma and Pennsylvania (Live Insurance News, 2026).

Those states teach something the letters do not address. Removing a predictive variable does not remove the premium, it redistributes it. The rate credit was carrying migrates to territory, prior claims and home characteristics; policyholders who benefited from strong credit see increases and those penalized by weak credit see decreases, while the aggregate need stays anchored to losses.

The transition is where that gets expensive. Repricing a book to drop a variable with a 2-to-1 or wider factor spread produces dislocation in both directions, and the newly surcharged best-tier segment is by construction the one with the most financial flexibility to shop. Retention turns asymmetric: the surcharged good-credit segment defects to carriers or channels pricing them better, the newly discounted segment stays, and the remaining pool's loss experience deteriorates against the rate level assumed at transition.

The capping mechanics carry their own trap. A carrier capping renewal increases at 10% a year while the uncapped indication for a formerly best-tier insured runs 25% holds the difference as systematic rate inadequacy that earns through for years, concentrated in its most desirable segment. That is why transition rules, renewal capping and multi-year glide paths dominate implementation in states that have actually removed variables. The pending bills in Iowa, New York, Oklahoma and Pennsylvania matter more for the transition rules they write than for the prohibition itself.

Further Reading

Sources

  1. Rep. Pressley: Lawmakers probe insurance companies on credit-based insurance scores
  2. InsuranceNewsNet: Warren, Pressley probe insurers on credit-based scores
  3. FTC: Credit-Based Insurance Scores report to Congress (2007)
  4. Experian: Which states prohibit or restrict credit-based insurance scores
  5. NAIC: Credit-Based Insurance Scores insurance topic
  6. Live Insurance News: State bills to ban credit scores in insurance pricing