Where AI Fits and Where Experience Still Matters in Underwriting

Underwriting has always centered on risk. What has shifted is how decisions get made and how quickly they surface.

For years, the process followed a familiar path. Applications moved through manual review. Medical history, prescriptions, labs and attending physician statements were evaluated against guidelines that had been refined over time. It worked. It still works. It just requires patience, especially when a case is layered or incomplete.

More recently, carriers began inserting automation at the front of that process. Large data sets can be reviewed almost immediately. Patterns are identified before a human ever opens the file. In certain situations, a decision is generated with little additional handling.

Both approaches are active today. In many cases, they are layered together inside the same workflow.

Traditional Underwriting Still Has a Role

Traditional underwriting depends on judgment. An experienced underwriter looks at context, not just codes. A diagnosis date, a medication change, or a lab trend over time. Those details matter when the risk does not neatly fit within a model.

This approach continues to carry weight for higher face amounts, more complex medical histories, and cases where the narrative behind the data is just as important as the data itself.

Manual review takes longer. There’s no way around that. If records are delayed or something doesn’t line up cleanly, the file can sit. Additional requirements are common once nuance starts to surface.

That does not make the process inefficient per se; it just reflects the level of review the case calls for.

Where AI-Driven Underwriting Fits

Automation changes the starting point. Data is gathered and assessed almost immediately. Prescription histories, claims data, and electronic records, for example, are reviewed early to determine whether the case qualifies for an accelerated path.

When the profile is straightforward, the experience feels different. Fewer follow-ups. Fewer additional requirements. A shorter distance between application and decision.

The friction tends to show up when the data tells only part of the story. If a diagnosis is coded inconsistently or the prescription history is stale, the model will flag it. That can mean an unexpected outcome or a sudden request for records that no one anticipated at submission.

At that stage, the file often moves back into a more traditional review lane.

Understanding how a carrier deploys automation, and where it tends to tighten or loosen, has become part of underwriting strategy.

What AI Is Actually Using

There is still confusion around what underwriting models pull in and how far they reach.

In employer-sponsored health coverage, you’re not dealing with facial recognition, GPS tracking or restaurant purchase data. The inputs are far more traditional than people assume. They are medical and claims-based.

AI systems typically analyze structured and permissioned data such as medical records, prescription history, lab results, claims experience and the information provided on the application itself. This is largely the same information that has always informed underwriting decisions. The difference is processing speed and pattern recognition across large volumes of data.

Lifestyle factors are generally captured the same way they have been for years. Application disclosures. Medical documentation. Coding tied to diagnoses or treatment patterns.

There are experimental models in other lines of coverage that look at broader behavioral inputs. In employer-sponsored health underwriting, the framework remains grounded in medical and claims information.

The technology may be new. The data sources are not.

Why Expectations Have Shifted

Accelerated decisions have reset expectations. When a case moves in days instead of weeks, that becomes the baseline. If another file takes longer or shifts late in the process, the frustration is rarely about underwriting itself. It is about the surprise.

Underwriting is often the first real moment where a client feels how the coverage is going to go. If it drags or changes late, that impression sticks. Implementation teams feel it. HR feels it. Employees feel it.

Delays can affect onboarding timelines. Uncertainty introduces anxiety at the exact point benefits are supposed to feel stabilizing.

As enrollment becomes more digital and turnaround times shrink in other areas of the benefits experience, underwriting stands out more than it used to.

How This Impacts Placement Strategy

Underwriting strategy no longer begins after submission. It starts earlier.

Carrier selection, positioning, expectation setting and documentation review all happen before the application is sent. A profile that looks clean on the surface may trigger model sensitivities depending on how the data is structured. Another case that appears complex may move smoothly if the information aligns well with a carrier’s thresholds.

The real shift isn’t choosing one model over the other. It’s knowing how a carrier is actually using each one and positioning the case accordingly. When that alignment is off, you find out quickly.

Speed matters. So does predictability. Most clients care about both.

The Bottom Line

No one replaced underwriting. They just built another layer on top of it. Some cases run straight through. Others still need someone to sit with the file and think.

The advantage now is understanding how those layers interact inside each carrier’s process. When the case and the workflow line up, placement feels steady. When they don’t, the friction shows up early.

That’s where experience matters. At CRC Benefits, we work closely with carriers across funding types and underwriting models, so we understand how their processes actually function in practice, not just how they are described. We help brokers position cases intentionally from the start, anticipate where automation may tighten or flex, and reduce surprises before submission.

When underwriting is aligned early, everything downstream gets easier. That’s the kind of partnership we bring to the table. Reach out to your CRC Benefits team with any questions. We are here to help!

Contributor: Tara Jones is a Benefits Sales Executive for CRC Benefits in North Carolina.

End Notes:

1. McKinsey & Company, The Future of AI in the Insurance Industry, July 15, 2025.
   https://www.mckinsey.com/industries/financial-services/our-insights/the-future-of-ai-in-the-insurance-industry
2. BizTech Magazine, How Artificial Intelligence Is Transforming the Insurance Underwriting Process, March 4, 2025.
   https://biztechmagazine.com/article/2025/03/how-artificial-intelligence-transforming-insurance-underwriting-process