Workflow Fixes That Stop Underwriting Evidence From Aging Out

Closing the Evidence-Recency Gap Without Slowing Underwriting Down

Most reinsurers already know their underwriting evidence can go stale. Very few have built a workflow that actually catches it before a decision gets made.

The reason is not lack of concern, it is that evidence age has traditionally not been tracked as a distinct variable at all. It sits inside the underwriting file, unmeasured, until a claims dispute or an actuarial review forces someone to look for it.

The strategic question of who owns this decision is addressed in the decision rights needed to control underwriting evidence that ages too quickly. This post is about the practical workflow changes that put that ownership into daily practice.

What Is the Single Highest-Impact Workflow Change?

Tracking evidence age explicitly, by evidence type, at the point of underwriting.

Right now, most underwriting files record what the evidence says, but not systematically how old it is relative to the decision date. That single gap is why stale evidence slips through undetected in the first place.

Adding an evidence-age field to the underwriting record, tied to a defined threshold per evidence type, turns an invisible risk into a visible, flaggable one. That is a small technical change with a large downstream effect on how much stale evidence actually reaches a final decision.

Should Underwriters Look at BMI History Instead of a Single Reading?

Yes, and this is one of the clearest fixes available right now.

RGA's research on GLP-1 underwriting makes the case directly: insurers need to consider "an applicant's BMI history rather than simply BMI at the time of application." A single number cannot show a trajectory, and trajectory is exactly what matters for fast-changing risk factors.

Building a trend view into the underwriting workflow, rather than a single-point snapshot, is a workflow decision, not a data-availability problem. The data to build that trend often already exists across prior applications, medical records, and pharmacy history, it simply is not being assembled into a trend by default.

Why Does a Trend View Catch Risk a Snapshot Misses?

Because it reveals direction and rate of change, not just current status.

A BMI reading taken today tells an underwriter where an applicant is. A BMI trend tells an underwriter where the applicant has been and how fast things are moving, which is a much stronger signal for risk that changes quickly.

This matters most for exactly the cases the aging-evidence problem is worst for, applicants whose relevant metrics can swing significantly within months.

How Should Recency Thresholds Vary by Evidence Type?

They should be set individually per evidence type, not applied as one blanket rule across the underwriting file.

Fast-decaying evidence needs a short refresh window. Weight, BMI, and blood pressure fall into this category, especially for applicants on treatments known to cause rapid change.

Slower-moving evidence can tolerate a longer window. Chronic condition history and structural health factors change less quickly, so a uniform threshold either over-refreshes low-risk evidence or under-refreshes high-risk evidence, wasting effort in one direction and leaving exposure open in the other.

What Triggers Should Force a Refresh Before Issuance?

Two triggers matter most, evidence exceeding its defined age threshold, and a material gap between application date and evidence collection date.

Both should be automatic, not left to underwriter discretion to remember and apply consistently. An evidence-age check built into the workflow catches this at the moment of decision, not after the fact.

The goal is not to slow every case down. It is to make sure the small percentage of cases where evidence genuinely has gone stale get flagged and refreshed before a treaty locks in pricing based on outdated information.

Can This Be Automated Without Slowing Down Accelerated Underwriting?

Yes, and this is where the workflow design actually matters most.

An automated evidence-age check runs quietly in the background of an accelerated workflow. It only escalates to manual review when a threshold is actually breached, which keeps the speed benefit of accelerated underwriting intact for the majority of cases that do not need intervention.

Tools purpose-built for this already exist. An Accelerated Underwriting AI Agent paired with a Non-Medical Limit Optimization AI Agent can enforce evidence-recency thresholds automatically without reintroducing the manual delay accelerated underwriting was built to remove.

What Role Does EHR Access Play in Fixing This?

Continuous or periodic EHR access gives underwriters a longitudinal view of an applicant instead of a single snapshot, which directly addresses evidence that ages inside the policy period, not just before issuance.

Munich Re's research on this topic notes that electronic health records offer "the most comprehensive view of an individual's health history, including lab results, prescription histories, and follow-up patterns." That longitudinal depth is exactly what a single-point exam or lab panel cannot provide.

Building periodic EHR re-pulls into the underwriting and in-force monitoring workflow, not just the initial application, is one of the more direct structural fixes available. It shifts evidence from a one-time snapshot to an ongoing signal, which is the actual target state this whole workflow problem is pointing toward.

How Should This Be Measured on an Ongoing Basis?

By tracking the distribution of evidence age across the in-force book, by evidence type, as a standing operational metric.

This is the same shift that turns evidence-recency from a one-off audit finding into a continuously monitored control. Set an internal threshold for what percentage of the book is allowed to carry evidence past its defined age window before it triggers a formal review.

That metric belongs on the same reporting cadence as other underwriting quality indicators, not treated as a separate, occasional exercise. Making it routine is what keeps the workflow fix from quietly decaying back into informal practice a year after it is introduced.

What Is the First Practical Step to Take?

Start with a single, focused audit of evidence age across one treaty's in-force block.

Pick a representative treaty, pull the evidence-collection dates for the underlying policies, and measure how much of that book is currently carrying evidence past a reasonable age threshold. That baseline number is usually the thing that turns evidence-recency from an abstract concern into a concrete, budgeted workflow project.

What Does a Pilot Program for This Look Like?

A focused pilot applies evidence-age tracking and refresh triggers to a single treaty or cedant relationship before rolling the workflow out across the full book.

Choosing a pilot scope deliberately matters more than moving fast. A pilot on a treaty with a meaningful concentration of GLP-1-relevant policies, or another category known to decay quickly, will surface the workflow's real value faster than a pilot on a low-risk, slow-changing book.

The pilot should run long enough to generate a real before-and-after comparison, typically two to three underwriting cycles, so the organization has actual evidence of impact before committing to a full rollout. That evidence is also what makes the case for budget and resourcing an easier internal conversation than a proposal based on projected benefit alone.

How Should This Workflow Be Vendor-Evaluated?

Evaluate any workflow or data vendor specifically on its ability to timestamp and track evidence age at the field level, not just on its ability to source or summarize the evidence itself.

Many underwriting data and automation vendors are strong on extracting and summarizing medical evidence but weaker on treating evidence age as a first-class, trackable attribute of every data point they handle. That distinction should be a specific evaluation criterion, not an assumed capability.

Ask any vendor under evaluation to demonstrate exactly how their system flags evidence approaching or exceeding a defined age threshold, and how that flag surfaces to an underwriter at the point of decision. A vendor that cannot answer this concretely is not yet solving the actual problem this post is describing, regardless of how strong their broader data capabilities are.

How Should Success Be Measured Once the Workflow Is Live?

Success should be measured as a reduction in the share of the in-force book carrying evidence past its defined age threshold, tracked quarter over quarter against the pre-rollout baseline.

That single metric is more useful than a broader, harder-to-interpret measure like overall underwriting cycle time, because it directly targets the specific exposure this workflow was built to close. A reduction in that percentage over successive quarters is direct evidence the workflow is working, independent of whatever else is changing in the broader underwriting operation.

It is also worth tracking separately by evidence type, since a workflow might close the gap quickly for prescription history while lagging on physical exam data, and that kind of detail is exactly what tells a team where to focus the next round of improvement rather than treating the fix as complete once the headline number improves.

This kind of granular tracking also gives underwriting leadership a defensible, evidence-based answer the next time an executive or auditor asks whether the workflow investment actually changed anything, rather than a general impression that things feel better than before.

From there, the fixes described in this post, evidence-type-specific thresholds, automated flagging, trend-based readings instead of single-point ones, and periodic EHR refresh, can be rolled out incrementally rather than all at once. Small, measured progress on this front compounds the same way the underlying risk does, just in the reinsurer's favor instead of against it.

Sources

• Munich Re: GLP-1 therapies and mortality risk: Implications for life insurers
• RGA: RGA GLP-1 Study: Weighing the Evidence