The Actuaries’ Problem
How perfect math builds perfect exclusion
In May 2023, State Farm announced it would stop selling new homeowners policies in California. The statement was three paragraphs long and cited “historic increases in construction costs” and “a challenging reinsurance market.” Within eighteen months, seven of the state’s twelve largest home insurers had either paused or restricted new policies. Allstate had already frozen sales in 2022. AIG and Chubb pulled back. By early 2025, the California FAIR Plan — the state’s insurer of last resort, designed as a temporary backstop — held nearly 452,000 policies, more than double its count from 2020. The backstop had become the market.
The podcast audio was AI-generated using Google’s NotebookLM.
Florida followed a different timeline but arrived at the same destination. Farmers left. Progressive reduced exposure. AAA retreated. Smaller carriers declared insolvency outright. In Louisiana, homeowners now pay roughly three times the national average for coverage. And these are just the states where the crisis is loudest. Insurify’s analysis flagged fifteen states at risk of the same pattern. The geography is expanding. It is no longer coastal.
The instinct is to read this as failure. Insurance companies fleeing. Homeowners abandoned. A system breaking under pressure. But that reading misses the more interesting question, which is whether the system is breaking at all — or whether it is doing precisely what it was built to do.
Insurance, at its origin, is one of the more elegant financial inventions
The basic mechanics are old: a group of people who face uncertain, potentially catastrophic loss agree to pool their resources. Each contributes a small, predictable amount. When catastrophe strikes one member, the pool pays. No individual can predict their own loss, but the group’s losses are statistically predictable. The law of large numbers converts individual chaos into collective order. This is beautiful mathematics. It transferred catastrophic risk from the person least able to bear it — the individual — to the entity best positioned to absorb it — the pool.
The modern insurance company is still doing this, technically. It still collects premiums. It still pays claims. But somewhere between the actuarial tables and the quarterly earnings call, the emphasis shifted. Kenneth Arrow identified the tension in 1963: insurance markets are inherently unstable because the insurer and the insured have different information about risk, and that asymmetry warps the market. Arrow was writing about healthcare, but the structure applies everywhere. The question stopped being “how do we pool risk?” and became “how do we identify risk?” — which is a different question with different consequences.
Here is how the shift works
An insurer’s profitability depends on the gap between premiums collected and claims paid. The wider the gap, the better. There are two ways to widen it: charge more, or pay less. But there is a third approach, subtler and more powerful than either — select better. If the insurer can identify which policyholders are likely to file claims and exclude them before they do, the pool becomes cheaper to maintain. The remaining customers are lower-risk, claims fall, and profitability rises. The insurance company has not improved outcomes. It has improved its portfolio.
The tools for this selection have become extraordinary. Satellite imagery assesses roof condition. Credit history predicts claim frequency. Zip codes carry actuarial weight that their residents cannot see. A homeowner in a coastal Florida county might have a perfect record — no claims filed, roof replaced last year, hurricane shutters installed — and still receive a nonrenewal notice because the county-level risk model recalculated. The individual record is irrelevant. The geography is the variable. The algorithm prices the portfolio, not the person.
(It is worth pausing on the precision involved. These are not crude instruments. The actuarial models processing risk across millions of households are among the most sophisticated prediction engines in commercial use. They are more accurate than most medical diagnostics. The science is sound. The math works. If you were running an insurance company — if your job were to keep the pool solvent and the shareholders satisfied — you would make the same decisions.
That is worth sitting with for a moment, because the system’s logic is not wrong. Its logic is what makes the consequences so difficult to argue with.)
So the logic holds. Every actor in the chain is behaving rationally. The insurer identifies risk. The reinsurer prices it. The shareholder demands a return. The regulator permits what the law allows. No one is making an error. No one needs to.
Now look at what that rationality produces
The better insurers get at identifying risk, the more finely they can segment their customer base, and the more people fall into categories that are unprofitable to cover.
Perfect information makes perfect exclusion possible.
The insurance pool — which only works when it includes a mix of high and low risk — fragments into ever-smaller subgroups, each priced according to its own statistical destiny. The low-risk pay less. The high-risk pay more, or find themselves priced out entirely. The pool that was supposed to distribute risk has become a sorting mechanism that concentrates it.
Consider what happens at the margins
A nonrenewal notice arrives in a standard envelope, the company logo in the upper left corner, the paper weight identical to the quarterly statement that preceded it. There is no red ink, no bold type, nothing to distinguish it from routine correspondence. The homeowner in a newly reclassified flood zone opens it and learns that premiums will double. The house, which was an asset yesterday, is a liability today — not because anything about the house changed, but because the model updated. The homeowner cannot sell at the previous value because the next buyer faces the same insurance costs. Cannot refinance because the lender requires coverage the homeowner cannot afford. Cannot stay because the carrying costs have exceeded the household budget. The actuarial table has made a determination about this person’s future. No human made a decision. The spreadsheet updated. And this is what the California exodus looks like from the inside — not a sudden catastrophe but a quiet reclassification, household by household, county by county, each one the product of a model recalculating, a threshold breached, a portfolio rebalanced, until the insurer’s map of California no longer matches the state’s map of itself, until the places where people live and the places where coverage exists are two different geographies, and the distance between them is not measured in miles but in basis points on a reinsurance treaty that the homeowner will never see and could not read if they did. State Farm did not leave California because homes were burning. State Farm left California because its models determined that the probability of homes burning, multiplied across its portfolio, exceeded the return threshold its shareholders required. The homes were still standing. The math had moved.
The math moves in one direction
Insurance companies do not return to markets they have left. Once the models have recalculated, the recalculation is permanent — or at least persistent enough to function as permanent. Louisiana homeowners paying three times the national average are not waiting for the market to correct. The market has corrected. This is the correction.
The state backstops — FAIR Plans, Citizens Insurance, federal flood programs — absorb what the private market discards. These programs were designed as temporary safety nets, last resorts for the small number of people who fell through the cracks. They are now absorbing hundreds of thousands of households in a single state. California’s FAIR Plan exposure has exceeded $650 billion. The backstop was never engineered for this load. (I keep returning to this point and I am not sure I have it right yet — but it seems like the backstop only functions as a backstop if the primary market still exists to define what “normal” coverage looks like. When the primary market leaves, the backstop isn’t catching exceptions anymore. It is the system. And a system designed for exceptions cannot operate as the default. Or maybe it can, and we just don’t have a word for what that becomes.)
Insurance does not distribute risk equally. It reveals which risks the system has decided to bear and which to abandon.
This distinction matters because it changes the nature of the problem
If insurance were failing — if the models were inaccurate, the pricing wrong, the companies mismanaged — there would be a fix. Better models, better regulation, better management. But the models are accurate. The pricing reflects real risk. The companies are well managed, by every metric that matters to their shareholders.
The system is working exactly as designed — which is where the trouble starts.
The actuaries’ problem is not that they cannot calculate risk. They can calculate risk with remarkable precision. The problem is that precise calculation, applied at scale, produces a result that nobody designed but everybody enabled: a growing category of people, properties, and places that the mathematics have determined are not worth protecting. The Uninsurables.
Whether those people understand the mathematics is, from the system’s perspective, irrelevant. The model does not require their comprehension. It requires only their data.
-Aimé