Human-in-the-Loop AI: What Insurance Agents Must Review

A tool drafts the case summary, flags a medication, and suggests a carrier. You skim it, it looks right, and you send it on. Was that human oversight, or was it a human watching software work? The phrase “human in the loop” gets used constantly in conversations about AI in insurance, and it is rarely defined. This article explains what it means for a licensed agent, what the NIST AI Risk Management Framework and the NAIC say that is relevant, and how to build review steps that actually catch mistakes.

What “human in the loop” means in practice

The term describes how much a person is involved in what an AI system produces. NIST addresses this directly in its AI Risk Management Framework. In Appendix C, the framework says that “Human roles and responsibilities in decision making and overseeing AI systems need to be clearly defined and differentiated.” It also describes the range of possible setups: AI systems “can autonomously make decisions, defer decision making to a human expert, or be used by a human decision maker as an additional opinion.”

Those are three very different arrangements:

• Autonomous: the software decides and acts, and a person sees the result afterward, if at all.

• Deferring: the software hands the decision to a person when it is unsure or when a rule says so.

• Advisory: the person decides, and the software offers an additional opinion.

For licensed agents, the advisory arrangement is the natural fit. You hold the license, you sit across from the client, and the carrier makes the final underwriting and issue decision. A tool that narrows a list or drafts a summary is an input to your judgment, not a substitute for it.

Why a human in the loop is not automatically a safeguard

Adding a person to a process does not make the process safe. The same NIST appendix warns that “Human-AI interaction results vary.” It notes that under certain conditions the AI part of the interaction can “amplify human biases, leading to more biased decisions than the AI or human alone.” When teams organize the human and the tool well, NIST says the result can be “complementarity and improved overall performance.”

The takeaway for an agency is practical. A review step that is a quick glance at a screen is a checkbox, not oversight. The value comes from designing the step so a reviewer can actually disagree with the tool, and knows when and how to do it.

What regulators expect on oversight

The National Association of Insurance Commissioners (NAIC) adopted its Model Bulletin on the Use of Artificial Intelligence by Insurance Companies in December 2023. According to the NAIC’s artificial intelligence topic page, the bulletin “establishes guidelines and expectations to ensure responsible use of AI by insurance companies” and “sets forth expectations as to how insurers will govern the use of AI.” The bulletin is addressed to insurers, not producers. Still, carriers that must show governance over their own AI use may reasonably ask their distribution partners how they supervise theirs. We walk through the bulletin in more detail in our article on what the NAIC’s AI rules mean for agents.

Adoption is uneven on the carrier side. The same NAIC page reports that, of the 161 life companies that responded to its survey, 58% reported using, planning to use, or planning to explore AI or machine learning models. In other words, you should not assume the carrier is applying a human review step you cannot see. Build your own.

Four review steps that do real work

Not every output needs the same level of scrutiny. Match the review to the consequence of a mistake.

1. Decide which outputs need a person before they leave the building

Anything that goes to a carrier, a client, or a file of record should have a named human reviewer. NIST’s Map function includes the outcome that “Processes for human oversight are defined, assessed, and documented.” For an agency, that can be a one-page list: which tool outputs exist, which ones are sent outside, and who signs off on each.

2. Give the reviewer something to check against

Reviewing a summary without the source material is guessing. Make sure the reviewer can see the original client answers, the carrier guideline or rate page the tool relied on, and the date. If the tool cannot show its inputs, treat its output as a draft only. Our piece on explainable AI in insurance covers what to ask a tool to show you.

3. Make override easy and normal

NIST’s Manage function calls for post-deployment monitoring plans that include “appeal and override.” In an agency, that means a reviewer should never feel they are fighting the software to change an answer. Agree on how to correct a tool output, where to record that you did, and what happens to the correction.

4. Track the misses

When a reviewer catches an error, write it down: what the tool said, what was right, and what kind of case it was. A short log of corrections shows you where the tool is weakest. It also shows whether reviewers are catching problems at all. A log that stays empty for months usually means rubber-stamping, not a flawless tool.

When automation is fine and when it is not

Some tasks carry little risk if the software gets them wrong: reformatting notes, drafting a calendar summary, sorting a task list. NIST itself acknowledges that some AI systems “may not require human oversight” while others “may specifically require human oversight.” A reasonable agency rule is to scale the review to the stakes:

• Low stakes, easy to reverse: spot-check.

• Anything a client will read or rely on: review every item.

• Anything that touches health information, eligibility, or what you tell a client a carrier will do: review every item and keep a record.

For the broader question of what to hand to software at all, see AI augmentation vs. automation.

A short agency checklist

1. List every AI tool in use and what it produces.

2. Mark which outputs leave the agency and name a reviewer for each.

3. Make sure reviewers can see the source inputs.

4. Write down how to override a tool and where to record it.

5. Keep a simple log of corrections and read it monthly.

6. Ask vendors how their tools support review and override before you buy. Our guide to vetting AI vendors has questions to start with.

The bottom line

Human in the loop is only meaningful when the human has the information, the authority, and the habit to disagree with the tool. Define who reviews what, give them the source material, make override routine, and keep a record of the catches. That is both better practice and the kind of governance story carriers and regulators increasingly expect to hear.

Peach Pilot supports licensed agents’ workflow. Carriers make final underwriting and issue decisions.