What Biotech Can Learn from Insurance: Actuarial Thinking in Drug Development

Biotech and insurance rarely appear in the same sentence unless one is covering the other. One traffics in breakthroughs and billion-dollar valuations. The other concerns itself with probabilities, premiums, and the statistically inevitable demise of us all. Yet, it might be time for biotech to stop aspiring to become the next Tesla of therapeutics and start learning something from its actuarial cousins.

Because if there's one industry that has truly mastered the art of modelling uncertainty, pricing risk, and staying solvent in the face of rare-but-catastrophic events—it is not venture capital. It is insurance.

Actuaries are, in many ways, the anti-entrepreneurs. They deal in cold probability. Their job is not to guess which idea might change the world, but which risks are mispriced by everyone else. Their models aren’t built to impress investors; they’re built to ensure the company doesn’t collapse when a flood wipes out half the Bavarian countryside or when 37-year-old dentists start dying at higher-than-expected rates.

This kind of thinking—precise, probabilistic, profoundly unsexy—is exactly what biotech could use more of.

Consider the way most biotech pitches still work. You show a total addressable market that is, somehow, always in the billions. You display preclinical data with a flattering y-axis. Then you deploy what can only be described as spreadsheet fan fiction: 5-year forecasts, tidy compound annual growth rates, and of course, our friend the discount rate. At no point do you seriously engage with questions like: What is the actual likelihood of technical failure? What happens if the trial is delayed by 18 months? If pricing pressure reduces reimbursement by 20%? These are not hypotheticals. They are actuarial facts waiting to be acknowledged.

Insurance companies model for fat tails. They know that rare events happen more often than the normal distribution allows. Biotech companies, on the other hand, often model success as a near-certainty, and then panic when their Phase II readout produces a p-value that sounds more like a rounding error.

In a rational world, biotech would build probabilistic models around each milestone. They would price not just the upside of success, but the downside of partial failure. They would communicate to investors that they are not in the business of guarantees, but of calculated exposure to asymmetrical risk.

Which, incidentally, is exactly what insurance companies do. They don’t promise no hurricanes. They promise a balance sheet that can absorb one.

Of course, the metaphor isn’t perfect. Insurers make money by avoiding claims. Biotech makes money by chasing the improbable. But both must live with the mathematics of uncertainty. And both, if they want to survive, must build systems that respect variance.

So perhaps it’s time we gave the actuaries a bit more respect. Not because they have better jokes—they absolutely do not—but because they know how to think about rare events, cumulative exposure, and the grim realities of survival.

In biotech, that kind of thinking isn’t just prudent. It might be the difference between the next unicorn and the next obituary.