Superintelligent Agents for Insurers: Claims and Underwriting

Superintelligent agents for insurance are a hypothetical next step: AI agents far more capable than today's, able to read a whole claim file, compare it with policy wording and prepare a complete case in the time it takes to pour a coffee. Even at that level, the sound design stays the same. The agent gathers, checks and drafts, while accountable people decide coverage, payout and risk acceptance. The useful work today is building the memory, permissions and review points that let capability grow without losing control.

Key takeaways

• Far more capable agents would change the speed and completeness of preparation work, not who is accountable for a coverage or payout decision.
• The strongest early fits are claims intake, document review and underwriting preparation, because they are heavy on reading, matching and summarizing.
• Every agent action should be classed as read, draft, or commit, and only people should hold the commit step for money and coverage.
• Evidence trails matter more as capability rises: each conclusion should point to the document, clause or note it came from.
• You can prepare now by defining one role, its sources, its approval boundary and a success measure you can observe.

What does superintelligent mean for an insurer in practice?

The word is a forecast, not a product. It describes systems that would outperform skilled people at most cognitive tasks, including the slow, detail-heavy reading that fills an insurer's day. Nobody can promise when, or whether, that arrives in a particular form. An insurer does not need to settle that debate to plan well.

The planning question is narrower: if the agent in your claims queue became several times better at reading, reasoning and cross-checking, what would you still want a named person to own? The answer is usually the same list. Whether a loss is covered. Whether a payment is made and for how much. Whether a risk is accepted, priced or declined. Whether a suspicion of fraud is raised against a real customer.

Those are decisions with legal weight, regulatory exposure and a human on the other end. A more capable agent makes the preparation around them better. It does not make them less yours. Frameworks such as the NIST AI Risk Management Framework and the OECD AI Principles point the same way: capability should come with accountability, transparency and human oversight proportionate to the harm a decision can cause.

How could far more capable agents change claims intake?

Claims intake is where a customer's worst week meets your process. Information arrives in fragments: a phone call, a web form, photographs, an email from a repair shop, a forwarded message from a broker. Staff spend much of the first stage just assembling the file.

A highly capable intake agent could do the assembly continuously and in the background. It could:

• Identify the policy, the insured party and the likely line of business from the first message.
• Ask the customer the missing questions in plain language, in the customer's language, and stop asking when the file is sufficient.
• Match incoming attachments to the right claim and flag duplicates or contradictions between accounts.
• Draft a first-notice summary with open items, so the handler starts with a clear picture rather than a pile.
• Send acknowledgements and status updates that stay within approved wording.

The boundary matters here. The agent can say what the process is and what is still needed. It should not say whether the claim will be paid. A sentence like "your claim is covered" is a decision, and it belongs to a handler or adjuster who has read the policy against the facts.

Here is an illustrative scenario. A small business reports water damage in a storage room at night. The agent opens the file, collects photographs and the time of discovery, and notices that the reported plumbing repair date falls inside the policy period but that an attached invoice mentions earlier work. It does not conclude anything. It lists the discrepancy, cites the invoice line, and routes the file to a handler with a short question to put to the customer. The handler decides what the discrepancy means.

For a parallel from a neighboring field, see how medical billing agents handle claims and denials with follow-up kept inside defined limits.

What can document review look like with a much stronger agent?

Document review is where raw capability would be felt most. Policies, endorsements, adjuster reports, medical or repair estimates, contractor invoices, police reports and correspondence can run to hundreds of pages, and the relevant sentence is often buried.

A stronger agent could read the entire file against the policy wording and produce a structured working document. The key is what that document contains and how it is built. A useful review pack has:

• A timeline of events, each entry tied to a source document and page.
• The policy clauses that appear relevant, quoted exactly, with the agent's reading kept visibly separate from the quote.
• A list of facts that are established, facts that are claimed but unsupported, and facts that conflict.
• Questions the handler may want to ask, ranked by how much they would change the outcome.
• A note of what the agent could not read or was unsure about.

That last item is a design requirement, not a courtesy. As agents get better, their errors become fewer but harder to spot, because the output looks polished. A review pack that always says where it is uncertain, and always links claims to evidence, lets a reviewer verify quickly instead of trusting blindly.

There is also a security point. Claim files contain text written by third parties, and some of it may be crafted to steer an AI system. The OWASP guidance on prompt injection and its Top 10 for LLM applications treat this as a real class of risk. Treat everything inside a customer document as data to analyze, never as instructions to follow, and keep the agent's tools narrow enough that a manipulated reading cannot trigger a payment or a policy change.

Teams that already run evidence-heavy reviews, such as compliance teams and internal audit teams, will recognize the pattern: the agent assembles evidence, a person signs the conclusion.

How would underwriting preparation improve?

Underwriting is judgment under uncertainty, and much of an underwriter's day is spent before the judgment: chasing missing information, reconciling an application with supporting documents, reading prior loss records and checking appetite and guidelines. This preparation is where a very capable agent could free the most time.

A preparation agent could build a submission brief for each risk containing:

• A normalized summary of the applicant and the exposure, with every field tied to its source.
• Gaps and inconsistencies between the application, attached documents and any external records you are allowed to use.
• The relevant sections of your underwriting guidelines, quoted, with the points where the case sits near a limit.
• Comparable past decisions from your own files, so the underwriter sees precedent and not just a rule.
• A short list of clarifying questions to send back to the broker, drafted for approval.

The agent does not accept, price or decline. It also should not quietly apply a rating factor the underwriter has not seen. Where regulation treats risk assessment and pricing in some insurance lines as a sensitive or high-risk use, as the EU AI Act does for certain life and health contexts, the case for clear human review and traceable inputs is stronger still. Check with your legal and compliance advisers which rules apply to your lines and markets.

The benefit is consistency as well as speed. When every submission arrives with the same brief structure, underwriters compare like with like, and senior staff can review a junior colleague's file in minutes.

Where must accountable people keep the decision?

The cleanest way to hold the line is to classify every action the agent could take. Use this decision guide:

• Read: the agent may open files, search records and retrieve policy text. Allowed by default, within the permissions of the role and the case.
• Draft: the agent may prepare summaries, letters, questions and recommendations. Allowed, with the draft clearly marked and nothing sent without the right approval level.
• Notify: the agent may send routine, pre-approved messages such as acknowledgements and requests for documents. Allowed for fixed templates, logged every time.
• Commit: the agent would change a reserve, approve a payment, deny a claim, bind cover, alter a policy or flag a person for fraud. Never allowed without a named person approving that specific action.

Two supporting rules make the guide hold up. First, the approving person must be able to see the evidence behind the recommendation, not just the recommendation. Second, the approver needs the authority and the time to disagree. If a handler is measured on throughput alone, approval becomes a rubber stamp and the control exists only on paper.

Authorization should be enforced in the systems, not in the agent's instructions. The OWASP Authorization guidance on least privilege and deny-by-default applies directly: the agent's credentials should simply be unable to issue a payment, however persuasive its reasoning or however a document is worded.

What should an insurer do now to prepare?

You do not need a future model to start. A narrow agent today, built on the right foundations, is also the foundation that stronger agents will inherit. A practical plan:

• Pick one role in one line of business, for example first-notice assembly for a single claim type or submission briefs for a single product.
• Write down the decisions in that role that stay with people, and the name or job title of who holds each one.
• List the sources the agent may read: policy wording, guidelines, claim notes, and which are off limits.
• Define permissions per action class (read, draft, notify, commit) and enforce them in the connected systems.
• Set an observable success criterion, such as handlers needing fewer clarification loops on assembled files, judged by sampled review of real files.
• Build a small set of test files, including awkward and adversarial ones, and rerun them whenever the agent, its tools or its sources change.
• Log what the agent read, drafted and sent, so any outcome can be reconstructed later.
• Review the first weeks with the handlers who use it, then widen scope only where the evidence supports it.

The second item is the one teams skip. Writing down who owns each decision, before the agent exists, is what keeps responsibility from drifting as the technology improves.

How does Be Human handle this?

Be Human designs, implements and operates personalized AI infrastructure for companies. A first engagement for an insurer would define one useful role, such as claims intake assembly or underwriting preparation, along with the memory and tools it needs, the approval boundaries for commit actions, and a success criterion that can be observed. Managed operations can then include monitoring, recurring evaluations, integration maintenance, permission review, incident response and controlled improvements, so that when the underlying models become more capable, the agent's scope widens deliberately and not by accident. Service is remote, in English and Portuguese, and decisions on coverage and payment remain with your people.

Frequently asked questions

Are superintelligent agents available to insurers today? No. The term describes systems more capable than any current one, and no one can reliably date their arrival. Current agents can already assist with intake, document review and underwriting preparation, and building those workflows carefully is the best preparation for more capable ones.

Could a very capable agent decide claims on its own? Technically it might produce decisions, but that is a governance question more than a capability question. Coverage and payout decisions carry legal, regulatory and customer consequences, and most insurers will want a named, accountable person to own them. A sensible design lets the agent recommend with evidence and lets the person approve or overrule.

What is the biggest risk in giving agents access to claim files? Two risks stand out: over-broad permissions and untrusted content. Agents should hold the minimum access their role needs, and text inside customer documents should be treated as data, never as instructions. Together with logging and regular test runs, these limits keep mistakes small and traceable.

How do we know the agent is helping and not just producing polished text? Measure outcomes on real work: sample assembled files and have handlers or underwriters grade them against the source documents. Track how often the agent's flagged gaps were real, how often it missed something, and whether clarification loops are shrinking. Repeat the same checks whenever the model, tools or sources change.

Be Human editorial guidance. Examples are illustrative unless explicitly attributed. Sources provide background; implementation choices depend on your workflow.