What Chief Actuaries Should Challenge in Biometric Pricing Models

The Question Most Biometric Pricing Vendors Have Never Been Asked

Biometric data vendors are very good at proving their models predict individual mortality risk accurately. They are rarely asked to prove anything about what happens when that same signal moves for an entire cohort at once.

That gap is not a vendor failing so much as a question nobody thought to ask, because the individual-level value of biometric data was the obvious selling point, and correlation risk simply was not part of the conversation. It is time that changed, and the Chief Actuary is the right person to change it.

What Is the Core Question a Chief Actuary Should Ask?

Whether the pricing model treats policyholders as independent risks, or whether it has been explicitly tested for correlated deterioration across cohorts following a population-level event.

This is a binary question with a specific, checkable answer. Either the model's validation process included a correlation stress test, or it did not, and most Chief Actuaries evaluating a biometric-informed pricing tool today have not yet asked it directly.

Asking it changes the entire conversation with a vendor or internal modeling team, from "does this predict risk well" to "does this predict risk well under the conditions that actually matter for portfolio solvency."

Why Does This Fall to the Chief Actuary Specifically?

Because correlation risk is fundamentally a portfolio-level statistical question, and evaluating whether an independence assumption holds under stress is core actuarial territory.

Underwriting is well positioned to evaluate whether a biometric signal predicts individual risk accurately. It is not the function built to evaluate portfolio-level statistical dependence, which is a different discipline with different tools, closer to how catastrophe and pandemic risk get modeled than how individual underwriting risk factors get validated.

The Chief Actuary's office already owns this kind of correlation modeling for other risk categories. Extending that ownership to biometric-informed pricing is a natural fit, not a new function that needs to be built from scratch.

Does This Require New Actuarial Capability?

Yes, in most cases, since correlation stress testing on biometric cohort data is a distinct skill from traditional individual mortality modeling.

Many actuarial teams are strong on individual mortality and morbidity modeling but have less built-out capability specifically around correlated deterioration testing for non-catastrophe risk categories. Closing that gap may mean new internal capability, a specialized hire, or a partnership with a firm that already does this kind of correlation analysis for other risk types.

What Should a Chief Actuary Ask a Third-Party Data Vendor?

Whether the vendor's model has ever been stress-tested for correlated movement across a population sample during a shared event, and what evidence supports the answer.

This should be a standard due-diligence question for any biometric data or scoring vendor, on the same tier as questions about data provenance and individual predictive accuracy. A vendor with a real answer, backed by testing, deserves more confidence than one offering only individual-level accuracy statistics.

A vendor without an answer is not automatically disqualifying, but it changes how much weight that vendor's biometric signal should carry in overall pricing until the gap is addressed.

How Should This Challenge Be Framed if the Vendor Has No Answer?

As a gap to be closed jointly, not a reason to abandon biometric data altogether.

The individual-level predictive value documented in wearable sensor research is real and well established, steps per day "provides additional segmentation of mortality even after considering traditional underwriting factors such as smoking status, BMI, blood pressure and other health indicators." That value does not disappear because correlation risk has not been tested.

The right response is to keep using the data while explicitly flagging and pricing for the untested correlation gap, not to discard a genuinely useful signal over a solvable modeling gap. Framing it as a joint problem to solve, rather than a reason to walk away from the vendor relationship, tends to produce a faster fix.

Should This Change How New Biometric-Informed Treaties Are Structured?

Yes, treaty terms should reflect whether correlation risk has actually been tested and priced for.

A treaty built on a biometric pricing model with documented correlation testing can be priced with more confidence than one built on an untested model. Where testing has not been done, treaty terms should build in a pricing or capital margin that reflects the unresolved uncertainty, rather than pricing as if the risk were fully understood.

This is a standard actuarial practice applied to a category of risk that has not traditionally received it. Uncertainty about a model assumption should show up in the price, not get absorbed silently as unstated risk.

How Does This Fit Into Broader Assumption Governance?

It should sit alongside other assumption governance items, like mortality improvement scale reviews, as a standing item the actuarial function owns and revisits on a defined schedule.

Reinsurers already have a governance rhythm for revisiting mortality improvement assumptions and other long-duration pricing inputs. Biometric correlation testing belongs in that same rhythm, reviewed on the same cadence, rather than treated as a separate, occasional side project.

Building it into existing governance also makes it easier to get organizational buy-in, since it is extending a discipline that already exists rather than introducing an entirely new process. Tools like an Underwriting Assumption Validator AI Agent can help flag when a biometric pricing assumption has gone unreviewed for longer than the governance schedule allows.

What Is the Risk of Not Raising This Challenge at All?

A reinsurer keeps expanding biometric-informed pricing on an untested independence assumption, growing the exposure with every new policy written under the same unexamined model.

Every policy priced under a model that has never been correlation-tested adds to the eventual exposure if that correlation risk turns out to be real. The cost of not asking the question does not stay flat, it compounds as the biometric-informed share of the portfolio grows over time.

This is exactly the kind of exposure covered from the data-ownership angle in the data, ownership, and escalation model for biometric risk correlation after population events, and from the board-reporting angle in how much balance-sheet exposure biometric risk correlation after population events creates.

What Does a Successful Vendor Collaboration on This Look Like?

A successful collaboration produces a jointly developed correlation stress test, run against real cohort data, with results both the reinsurer and vendor agree accurately represent the risk.

The most productive version of this conversation treats the vendor as a partner with a shared interest in the outcome, not an adversary being audited. Most reputable biometric data vendors want their models trusted at the portfolio level, not just the individual level, because that trust is what sustains long-term adoption of their product.

A well-run collaboration typically starts with the reinsurer providing anonymized cohort-level outcome data from a known population event, and the vendor running its model against that data to see how well predicted individual risk translated into actual portfolio-level outcomes during the event. Where the model performs well, that becomes documented evidence supporting continued or expanded use.

Where it does not, that gap becomes the specific, scoped problem the vendor and reinsurer work to close together, rather than a vague concern that never gets resolved because it was never made concrete enough to act on.

How Should This Affect Vendor Selection for New Biometric Data Partnerships?

Correlation testing capability should be a formal, weighted criterion in vendor selection for any new biometric data partnership, not an afterthought raised only after a contract is signed.

Most vendor evaluation processes today weight individual-level predictive accuracy heavily, and rightly so, since that accuracy is the core value proposition. Adding correlation testing capability as an explicit, scored criterion changes vendor behavior over time, since vendors respond to what buyers actually evaluate and reward.

A reinsurer that consistently asks this question during procurement, across every biometric data vendor relationship, is contributing to a broader shift in how the vendor market approaches this problem, not just solving it for one relationship at a time.

How Should Existing Vendor Contracts Be Handled if They Predate This Standard?

Existing contracts should be revisited at the next renewal or amendment point with a specific request for correlation testing evidence, rather than left unchanged until the full contract term expires.

Renegotiating an active contract purely over this issue is rarely worth the friction, but most vendor relationships have natural touchpoints, contract renewals, service reviews, or feature expansion discussions, where this request can be introduced without disrupting an otherwise working relationship. Raising it at one of those natural points, rather than waiting for full contract expiration, closes the gap faster without requiring an adversarial renegotiation.

A reasonable interim step for a contract still years from renewal is requesting whatever correlation-relevant data the vendor already has, even informally, while formally building the correlation testing requirement into the next contract cycle. That keeps the relationship on track while still making measurable progress toward closing the actual exposure this post has described.

Raising this challenge is not a criticism of biometric data as an underwriting tool. It is the actuarial discipline the tool has always needed, applied at the point in its adoption where the portfolio-level stakes are large enough to make ignoring the question genuinely expensive.

Sources

• Munich Re: Physical activity measured by wearable sensors can effectively assess mortality risk
• RGA: Managing Pandemic Risk After COVID-19: Lessons from the past, preparing for the future