Quantum Computing and Insurance Pricing
From Satellite Photos to Live Drone Footage: What Quantum Computing Actually Means for Insurance Pricing
Introduction: Why I Fell Down the Quantum Computing Rabbit Hole
Hi. I’m an insurance agent named Wayne Watley. I like going down rabbit holes. I guess I’m a glutton for punishment.
I spent the last month trying to understand quantum computing. Not because I wanted to sound smart at industry events, but because I kept hearing it mentioned alongside AI and wanted to know if it was real or just another nothing burger.
Most trends I research end up being exactly that. A few minutes of digging and it becomes clear it’s hype wrapped in buzzwords. This one was different.
The moment it clicked for me was when I stopped thinking about quantum computing as “faster computers” and started seeing it as a response to a problem we are already hitting. Energy. Heat. Efficiency. Classical computing is running into real physical limits, and that is not something you fix by adding more servers.
Once I understood that, I stopped rolling my eyes and started paying attention.
The Problem Insurance Has Right Now
Here’s what I see every day.
A client’s situation doesn’t change. Same house. Same roof. Same maintenance history. No claims. Then renewal hits and the premium jumps hard, or they get non-renewed entirely.
When we dig into it, the issue usually isn’t their individual risk. It’s that the carrier suddenly realized an entire region, construction type, or exposure stack is riskier than they thought.
That tells me something important.
The system didn’t really understand the risk until after damage had already occurred.
Insurance doesn’t struggle because people are incompetent. It struggles because the models can’t keep up with how complex the real world actually is.
Weather patterns, building materials, labor costs, infrastructure age, population density, supply chain issues. All of these interact with each other. Today’s systems simplify that complexity because they have to. They bucket risk, average outcomes, and rely on assumptions.
That works until reality drifts far enough that the math breaks. When that happens, you see sudden underwriting freezes, broad non-renewals, and rate shock.
The Satellite Photo Problem
I didn’t come up with this analogy, but it clicked immediately when I heard it.
Insurance pricing today feels like satellite photos taken once a year. You get an update after the fact. Losses happen, then the model adjusts, then rates move.
It gives you the general shape of things, but it misses what’s changing in between.
Traditional insurance pricing operates on long cycles. Annual rate reviews. Quarterly loss development analysis. Heavy reliance on historical data.
That makes the system reactive instead of proactive.
What should take minutes takes days. What should take days takes months. The system is always behind.
What Quantum Computing Actually Enables
Quantum computing isn’t about making AI smarter or computers faster in the traditional sense.
It’s about processing complexity that classical systems can’t handle efficiently.
Think of it like live drone footage instead of satellite photos. Not perfect prediction, but awareness. Seeing more of what’s happening while it’s happening.
In practical terms, this means analyzing thousands of interacting risk factors at the same time. Running simulations across vastly more scenarios. Understanding correlations that current systems simplify or ignore.
Classical computers process these problems sequentially. Quantum systems explore multiple possibilities simultaneously.
That’s not science fiction. That’s physics.
Major financial institutions are already experimenting. Allianz has partnered with quantum startups to improve dynamic pricing models. HSBC has demonstrated quantum-enabled risk analysis that dramatically reduced processing time.
These aren’t promises. They are early signals.
The Three Things That Actually Matter for Insurance
After stripping away novelty and jargon, three things matter. Everything else is optional.
Earlier and Clearer Understanding of Risk
Insurance struggles not because it lacks data, but because it recognizes risk too late. Losses happen first. Models update later. Rates spike after that.
Any technology that shortens the gap between reality changing and insurance responding is meaningful. Anything that adds complexity without improving timing is noise.
Stability Over Precision
Clients don’t expect perfection. They expect fewer surprises.
If better computing leads to smoother adjustments instead of panic-driven swings, that builds trust. If it only leads to sharper pricing with more volatility, that’s not progress for the people paying the premiums.
Explainability
Better models are useless if no one can explain the outcome.
Insurance already struggles with the “because the model says so” problem. If advanced tools make decisions harder to explain, the system becomes more opaque, not more trustworthy.
What Actually Concerns Me
My biggest concern isn’t the technology. It’s the uneven pace at which it will be adopted.
If only a handful of large carriers or reinsurers gain better modeling earlier, they get to exit markets, tighten underwriting, and reprice risk sooner. Everyone else reacts later, usually after losses hit.
That creates more volatility, especially for consumers insured with carriers on the wrong side of that gap.
Better risk understanding doesn’t automatically mean gentler outcomes. Precision can feel like punishment if it isn’t handled carefully.
Market Is Already Moving
Investment in quantum capabilities across finance and insurance is accelerating.
Major carriers are allocating capital and forming research partnerships. They view quantum technology not as a distant possibility, but as a long-term competitive necessity.
This is no longer speculation. It’s allocation.
What This Means for Insurance Agents
If your value is paperwork and quoting the same carrier every year, better technology will eventually replace that.
If your value is interpreting risk, explaining change, and helping clients act before problems show up, your value increases.
More complex models make translation more important, not less.
Someone still has to explain why insurance feels different than it did five years ago and why doing nothing is no longer neutral.
How I Talk About This With Clients
I’m not teaching quantum computing in client meetings.
I lead with outcomes. Stability. Clarity. Fewer surprises.
If the conversation goes deeper, I frame it as a long-term shift, not a product or feature. Something that may help pricing make more sense over time, not something affecting their renewal next month.
My best test case is my mom, who is also my client. If I can explain it to her without losing her, I know I’m on the right track.
The Unresolved Question
The open question isn’t whether quantum computing changes insurance. It’s whether agents are brought along or left reacting after the fact.
If the math improves but explanations don’t, the experience may feel unchanged. Rates move. Appetite shifts. And the explanation remains vague.
That gap concerns me.
Why I’m Paying Attention
I’m not evangelizing quantum computing. I’m not promising revolutions.
I’m paying attention because insurance tends to encounter these shifts late and then act surprised when the ground moves.
If I can help clients feel oriented instead of confused when things change, this conversation has done its job, even if we never say the word “quantum” out loud.
And if future premium increases come with better explanations than “the carrier took a rate increase,” that’s progress worth watching.
About the Author
Wayne Watley is an independent insurance agent based in Shreveport, Louisiana and the owner of Watley Insurance Group. He works with individuals and business owners across the Ark-La-Tex region, focusing on helping clients understand risk before it turns into surprise costs.
His writing centers on clarity, long-term thinking, and explaining industry shifts before they become headlines.
