Post-issue sampling: Why life insurers should double down

The broccoli of life insurance

If you’ve ever sat in an underwriting strategy meeting with reinsurers, you’ll know the drill: concerns about mortality deterioration, stats on misrepresentation, and then… silence when someone mentions post-issue sampling. This work really is the broccoli of life insurance. Everyone agrees, it’s good for you and helps keeps the life insurance pool healthy and honest, but no one’s excited to add more to the plate.

Yet in 2025, post-issue sampling – also known as post-issue audits - isn’t optional. Reinsurers rely on it to validate pricing. Regulators expect it for governance. Chief underwriters depend on it to spot misrepresentation and improve the customer journey. So why is it still under-resourced and how can AI make a difference?

Why post-issue sampling matters

On paper, the benefits are obvious:

• Fraud Detection: Around 20% of sampled cases reveal adverse findings.
• Portfolio Quality: Reinsurers use post-issue sampling to ensure ’standard lives’ are truly standard — not misclassified risks.
• Regulatory Compliance: With increasing scrutiny from the FCA and US state regulators, a documented sampling process is a strategic asset.
• Operational Feedback: Insights from sampling improve distributor training, application design, and rules engine performance.

But here’s the catch: you only get value if you sample enough. 1% won’t cut it. 15% is more than adequate— but human teams can’t scale that without burning out.

Why do underwriters groan when they hear post-issue sampling? Because it’s hard work:

• Volume and Time: Ordering GP reports and chasing medical records is tedious especially as only 50–60% are returned within a decent timeframe.
• Inconsistency: Underwriters are human. Regularly reviewing 200-page detailed reports? Things get missed.
• Cost: Every doctor’s report and underwriter’s assessment time adds up. Big carriers can absorb it. Smaller firms? Not so much.
• Targeting: Random sampling and basic red flags waste effort and can miss high-risk cases.

Why reinsurers push for it

Reinsurers aren’t just being thorough — They’ve got skin in the game:

• Reinsurers carry long-tail mortality risk
• Post-issue sampling helps ensure competitive claims pricing, as it’s feedback to ensure quick, straight through processing journeys are effective. They also provide a guard against misrepresentation.
• Audit insights become consulting value-adds, to help balance competitive pricing vs quick and effective journeys.

The chief underwriting officers’ perspective: guardrails and gotchas

For chief underwriters, post-issue sampling is both shield and sword. A shield because it demonstrates governance and protects the portfolio. And a sword as it justifies changes in distributor oversight or risk appetite.

But what is the CUO’s biggest fear? Missing a major issue and not being able to take corrective actions when patterns of risk emerge. And so what chief underwriters want from their post-issue sampling is:

• Accuracy – to capture significant issues
• Scalability – audit enough to generate meaningful insights.
• Actionable insights – improve distributor oversights, refine underwriting, and adjust pricing strategy.

Is AI a game changer?

AI is transforming post-issue sampling — but it’s not a silver bullet.

What AI does well:

• Summarises medical evidence consistently. No more variance between underwriters and Monday vs Friday underwriting variance.
• Spots patterns of nondisclosure. Over time, models can learn which distributors, geographies, or demographics are higher risk.
• Frees underwriters from the paper chase. If 60–70% of the job is reading reports, why not outsource this and ask AI for a summary.

What AI struggles with:

• Perfection. Tools are ‘good but not perfect’. Some carriers are happy with 80% accuracy; reinsurers want around 95%. Missing a small extra mortality calculation now and again is survivable; missing declined cases is not.
• Instilling Trust. AI’s imperfections are not unlike entrusting a junior underwriter with key work. It requires transparency and oversight to build trust before you gain the efficiency benefits of full production levels.
• Explainability. Regulators (and chief underwriters) hate black boxes. If you can’t show why the AI made a call, you can’t defend it in court. AI won’t replace underwriters — but it can empower them. The future is underwriter plus machine, not underwriter versus machine.

Why insurers should double down

Skipping post-issue sampling is penny-wise, pound-foolish. Early-duration claims are expensive and reputationally damaging — and sometimes preventable.

More sampling means:

• Fairer pools. Honest applicants aren’t subsidising those who miss disclosures, where it would be fair to charge more
• Better pricing discipline. Reinsurers are unlikely to offer favourable terms to insurers with weak underwriting controls
• Improved automation. Sampling findings are fed back into the application questions and rules engine which in turn increase straight-through processing safely.

With AI handling the grunt work, scaling to 8-12% sampling rates is no longer a fantasy — it’s an achievable standard. And using AI and analytics, these samples can be targeted to get the most bang for the buck on undertaking post-issue sampling.

Underwriters vs. the machines: collaboration, not competition

Let’s be honest — underwriters aren’t afraid of post-issue sampling. What unsettles many is the idea that a machine might now be ‘better’ at their jobs. But the reality is more nuanced.

AI can process vast medical records with speed and consistency, catching things even seasoned underwriters might miss — especially when faced with 200 pages and a tight deadline. But AI also misses what humans catch: context, subtle inconsistencies, and that gut feeling when something just doesn’t add up.

The real tension isn’t about capability — it’s about identity. Underwriters have built long careers on judgment, experience, and the ability to spot risk in seconds. Being told an algorithm can do it faster? That stings.

But here’s the opportunity: most underwriters would gladly hand off the repetitive 70% of time reading reports and focus on the meaningful 30%: edge cases, judgment calls, and strategic conversations with distribution.

The future of post-issue sampling isn’t underwriter versus machine — it’s underwriter plus machine. Underwriters will continue to provide final sign-off, now supported by smarter tools and deeper insights.

Conclusion: From compliance to competitive edge

Post-issue sampling is evolving. It’s no longer a compliance checkbox — it’s strategic weapon.

• For reinsurers: It validates risk and pricing.
• For insurers: It protects portfolios and supports automation.
• For underwriters: It’s the safety net that enables innovation.

Operationally, it’s still a grind. Strategically, it’s priceless. And with AI in the mix, maybe — just maybe — we can move from nibbling broccoli to enjoying the full meal.

Want to learn more?

Discover how UnderwriteMe can help your team scale post-issue sampling with AI-powered underwriting tools. Click here to learn more.