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When Claims Start Moving at Machine Speed

Summary

Decisions have always been loops. A claim decision has a before and an after: the claimant responds, new facts arrive, and the insurer decides again.
They have always run on limited information and evolving context. Facts were partial, investigations costly, and regulation, courts and public opinion shifted while claims were still open.
The knowledge and process behind them were tacit, implicit and partial. Framing, assumptions, expected responses and escalation triggers lived in experienced people, not in systems.
AI changes the problem space dramatically.
- Scale: far more decision loops running at once, so claims stop being independent and patterns form across the portfolio.
- Coordination: claimants, and the parties around them, find each other and act together.
- More capable claimants: AI tools can help claimants examine policy language, locate relevant guidance and precedents, and pursue challenges at much lower marginal cost.
- Persistence: AI-assisted follow-up, appeals, and escalation can continue with far less effort, changing the economics of prolonged disputes.
- Speed: events move faster than existing review cycles and governance were designed for.
Claims processes face a different operating environment. Assumptions built around slow, costly and isolated claimant responses need re-examination.
Insurers will need AI across key parts of the decision cycle: world model, framing, investigation, simulation, decision support, action and monitoring. Human authority remains essential where claim rights or capital are committed.
AI is also fallible. It can hallucinate, misread evidence or apply a flawed frame at scale. AI-related error risk must be assessed in proportion to the decision’s consequences and potential portfolio spread, with integrity testing and independent review where warranted.
This means deconstructing the existing decision system and embedding it in the new world: making tacit judgment explicit, then rebuilding it as a continuous, simulated and monitored loop.
Embedding is not enough; insurers must actively embrace the new world. That means designing for trajectories, tempo and portfolio effects rather than automating yesterday’s workflow.

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1. Decision-making is richer than it looks

Claims decisions are often drawn as information, decision, action. In practice, they rest on a much richer decision system.
A decision-maker starts from a world model: assumptions about claimants, fraud, litigation, regulation, institutional priorities, and how similar situations evolve.
That world model draws on experience, pattern recognition, institutional memory, peer practice, prior outcomes, and the external environment.
The world model shapes the frame: routine, ambiguous, fraudulent, persistent, potentially litigious, or something else.
The frame determines what information matters, what more is sought, and how ambiguity is resolved.
Judgment resolves tradeoffs that rules cannot: cost, fairness, defensibility, customer impact, regulatory risk, precedent, and uncertainty.
Much of this is tacit. Organizations document the decision far better than the assumptions, framing, and institutional knowledge behind it.
Every consequential decision also carries an implicit view of how the claim may evolve: not one predicted response, but a range of possible trajectories.

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2. The claim decision is only the first move

Running example: suppose a homeowner files a claim for water damage. The adjuster attributes much of it to long-term seepage excluded under the policy, pays a portion of the claim and denies the rest.
The decision does not end the process. It changes the environment and starts a second loop on the claimant side.
The homeowner interprets the letter, gathers information, chooses a response, and acts: appeal, complaint, or public review.
The response can add facts and change the insurer’s understanding of the situation.
New evidence changes the information. Unexpected behavior can change the frame from routine to persistent, adversarial, or potentially litigious.
Repeated outcomes across claims can eventually change the insurer’s world model itself.
The pre-AI world already had two interacting loops: insurer (world model, frame, information, judge, act) and claimant (interpret, gather, choose, act). Both were human-mediated, slow, costly, and weakly coordinated.
The central question is not only whether the decision was defensible, but what it sets in motion and what would cause us to revisit the information, frame, or world model.

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Figure 1. Pre-AI: two human-mediated decision loops with slower feedback and limited scale.

3. AI changes both sides of the loop

AI does not simply automate the old workflow. It enters the insurer’s world model, framing, information, decision support, action, and monitoring.
It can improve discipline by externalizing assumptions, evidence, reasoning, and expectations that were tacit.
It also amplifies shortcomings: a weak frame, stale world model, or hidden bias can now be applied tens of thousands of times.
Humans remain involved where uncertainty, values, risk appetite, or accountability require it; the boundary between automated and human judgment becomes a design decision.
Verification of delegated authority becomes a first-class step. The carrier sets decision rights beforehand; the workflow checks that the proposed action and its designated owner fall within approved limits, escalating exceptions. The accountable owner must consider precedent and portfolio effects and, when necessary, defend the decision before regulators, customers or courts.
The claimant loop changes too. The insurer increasingly faces an AI-augmented counterparty.
In the example, the homeowner’s assistant reads the policy, contrasts “seepage” with “sudden discharge,” finds similar disputes and regulator guidance, and drafts an appeal in an evening.
AI can narrow the insurer’s historical advantage in legal resources, information, and patience. Automated follow-up lowers the claimant’s cost of pursuing a dispute.
Mental models built on the old asymmetry, such as “most partial denials are not challenged,” become less reliable.

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Figure 2. With AI: AI throughout the insurer stack, a more capable counterparty, faster loops, and portfolio-scale effects.

4. Investigations and information needs expand

AI-driven decisions are only as good as the information behind them, and AI makes the appetite for information grow, not shrink.
Observation is a capability the insurer chooses to spend on: field adjusters, engineers, forensic experts, special investigation units, third-party data, imagery, IoT and device data.
As claimants become better informed and more assertive, insurers must document more, verify more, and defend more of each decision.
In the example, the homeowner’s appeal includes a plumber’s statement disputing the seepage finding. Depending on the coverage question, the insurer may need an independent engineering assessment, prior claims history, or other relevant evidence.
The claimant side investigates too: second opinions, public adjusters, records requests, and AI-assisted searches for comparable claims.
Investigation becomes iterative rather than a one-time step. Each response can trigger a new round of information gathering on both sides.
This raises cost and cycle time, so the decision of when to investigate, how deeply, and with which tools becomes a judgment in its own right.
Investigation records also become evidence later, before regulators, courts, or the public, so they need to be complete and defensible.

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5. Scale changes the system, not just the volume

The bigger change is not only speed, but vastly more loops running concurrently.
At higher volumes, claims stop behaving like independent transactions.
Patterns emerge across claims. Insurers can detect them, and so can claimant-side AI.
In the example, homeowners with similar “seepage” denials find each other online, compare letters, and notice the same phrasing.
Social networks and digital discovery turn isolated complaints into coordinated action.
One possible escalation path is: single claim → recurring pattern → coordinated response → regulator or public attention → enterprise risk.
Individually rational claim decisions can add up to a damaging portfolio outcome.
The enterprise must reason at two levels: claim level, is this decision still right; portfolio level, what are thousands of similar decisions collectively creating?

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6. Tempo becomes a variable

The loop’s speed also changes, and neither side fully controls it.
Claims operations are calibrated to familiar tempos: acknowledgment deadlines, review cycles, appeal windows.
Compressed tempo: a viral post or media story can turn a weeks-long appeal into a decision needed in hours, before normal information gathering is complete.
Elongated tempo: a regulator inquiry, market conduct exam, or class action can stretch the loop across quarters or years, draining legal, compliance, and management attention.
In the example, the homeowner’s post spreads in days while the resulting regulator inquiry lasts eighteen months.
Both stress the system: one overwhelms speed, the other exhausts endurance.
Claimant-side AI can choose tempo strategically: accelerate publicly or persist at near-zero cost.
Decision systems need tempo awareness and the ability to switch playbooks, escalation paths, and staffing as the loop speeds up or slows down.

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7. The external environment becomes part of the loop

Regulation, courts, media, market expectations, and public opinion were often treated as background context.
In an AI-mediated environment, they become part of the feedback loop.
Insurer actions create narratives. Claimant coordination attracts regulators. Litigation reveals patterns.
The cast expands: plaintiff firms, journalists, state attorneys general, reinsurers, and the board can enter a dispute that began with one homeowner.
A regulatory response can change how thousands of open claims should be framed.
The environment is not only an input to the world model; portfolio interactions partly produce it.
The loop becomes recursive: decision → response → coordination → external reaction → changed world model → new decision.
Past experience still matters, but its weight falls when counterparties, coordination, and escalation paths change faster than precedent.

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8. Simulation becomes a core activity

Two capable loops, expanding investigations, portfolio coupling, variable tempo, and an active environment create too many combinations to reason about intuitively.
For consequential claims, simulation can inform decisions before action and as new evidence arrives.
A simulation plays the loop forward: claimant response, further investigation, new parties, tempo, and possible end states.
In the example, simulation could examine paths to acceptance, appeal, coordinated action or regulatory attention, with plausibility tied to evidence and assumptions.
Decisions are then informed by a range of plausible trajectories, not only immediate correctness. A decision defensible today but fragile across many paths is different from one that holds up broadly.
Simulation makes expected responses explicit, so deviations can trigger re-observation, reframing, or escalation.
At portfolio level, aggregated simulations can reveal patterns and stress points before they surface externally.
Simulation does not predict the future; it maps the possibility space, as war-gaming and bank stress tests do.

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9. AI can be wrong: pricing and testing for error

AI can hallucinate, misread documents, misclassify situations, and reason confidently from wrong premises.
One flawed model or prompt can affect tens of thousands of claims before anyone notices.
Claimant agents can also be wrong: fabricated precedent or misread policy terms still have to be handled correctly.
In the example, an agent that mislabels sudden pipe failure as seepage can create the very pattern that attracts regulatory attention.
For consequential AI-supported decisions, assess likelihood of error, cost if wrong and potential portfolio spread. Match testing effort to those risks.
Recommendations should be tested before action: evidence traced to source, reasoning checked for consistency, and outcomes compared across similar claims.
Where methods do not yet exist, insurers need adversarial tests, independent model cross-checks, sampled human review, and simulated error scenarios.
Integrity testing can also preserve evidence of what was checked and when, rather than leaving only an AI-generated recommendation.

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10. What insurers should build

Externalize tacit judgment: capture framing, assumptions, tradeoffs, and escalation triggers, then embed them in agent context, policies, and audit trails.
Maintain a dynamic world model combining claim data, portfolio patterns, regulation, litigation, media, and claimant behavior.
Treat framing as an explicit, revisable step, not a hidden assumption.
Build investigation capability that scales: decide when and how deeply to investigate, and preserve defensible records.
Build a simulation layer that runs trajectories for consequential claims and aggregates them across the portfolio.
Verify delegated authority at execution: confirm that the proposed action is within approved limits and that the designated decision owner is authorized to act or approve an escalation.
Preserve context at execution: retain the contemporaneous evidence, policy and rule versions, assumptions, approval and delegated authority.
Scale integrity testing and AI-error assessment to the consequences of the proposed recommendation or decision.
Add tempo awareness: detect compression or elongation and switch playbooks, staffing, and escalation paths.
Design action for multiple targets where needed: claimant, claimant community, regulator, legal, communications, and management.
Create portfolio learning and coordination so individually reasonable agents do not collectively create an unseen enterprise pattern, and so claims, legal, compliance, risk, and communications share one evolving picture.

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11. A new operating model for machine-speed claims

The emerging architecture is a continuous decision loop, not a pipeline.
Observe: gather claim information, investigation results, responses, portfolio patterns, and external signals.
Orient: update the world model and frame; test whether prior assumptions still hold.
Simulate: play forward plausible trajectories for the options under consideration.
Verify delegated authority: confirm that the proposed decision and its approver fall within the authority established before execution; route exceptions to the authorized escalation path.
Decide: exercise judgment under uncertainty, tradeoffs, competing objectives, and risk appetite.
Verify: before action, check source evidence, reasoning, and AI-error risk; preserve the validation performed.
Act: carry out the authorized action and preserve the rules, evidence, approval and authority in force at execution.
Monitor: compare what unfolds with simulated trajectories and expected tempo; re-enter the loop on deviation.
The executive question shifts from “Was this the right claim decision?” to “What did this decision set in motion, and does our judgment still hold as the world responds?”

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12. References and intellectual grounding

John Boyd’s OODA framework (Observe, Orient, Decide, Act) models decisions where action changes the situation and feedback updates orientation.
Boyd’s richer formulation matters here: Orient is shaped by experience, new information, analysis, synthesis, and interaction with the environment.
Boyd also emphasized tempo: operating inside an adversary’s loop, directly relevant to the dynamics above.
In insurance, the distinctive challenge is the interaction of thousands of decision loops across a portfolio, where individual actions can accumulate into enterprise-level consequences.
Defense war-gaming and banking stress tests offer precedents for trajectory-based decision support.
Doron Avital’s work on judgment under ambiguity and irreversibility informs the Decide component for high-consequence calls.
References: John R. Boyd, The Essence of Winning and Losing (1995–96); Doron Avital, Logic in Action (2012).

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About the authors

Upendra Belhe, Ph.D., is President of Belhe Analytics Advisory and a former senior P&C insurance executive at Chubb and Gen Re. His work focuses on decision ownership, delegated authority, and governance at execution across underwriting, claims, and AI-driven insurance workflows.

Website: belheanalytics.com | LinkedIn: linkedin.com/in/upendrabelhe

Venkata Pingali, Ph.D., is the founder and CEO of Carver Agents, building legal and regulatory infrastructure for AI agents. His work focuses on making legal requirements actionable within agent systems.

Website: carveragents.ai | LinkedIn: linkedin.com/in/pingali

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