Claims arrive
Documents enter a shared queue instead of individual inboxes.
The storm surge scenario¶
Example
A massive storm is on the horizon
The claims team knows a flood of work is coming. The goal is not simply to move faster—it is to expand capacity while keeping evidence, checks, and accountability visible.
Before landfall¶
The team agrees on the violation types and policy questions it wants the tool to look for. Claims Processor is prepared with the current review logic, and team leads decide which findings need extra scrutiny.
During the surge¶
First review
An AI reviewer reads the full file and identifies possible issues with page references.
Second look
Additional reviewer logic can challenge the finding, check completeness, or identify disagreement.
Human decision
An adjuster reviews the evidence, adds notes, and confirms or rejects each issue.
Instead of asking every person to read every page from a blank start, the tool creates a structured first pass. Human attention can move toward exceptions, high-severity claims, low-confidence findings, and disagreements between reviewers.
How the team reaches 10× capacity¶
The “10×” opportunity comes from combining several improvements:
Parallel reading: many claim files can be prepared at the same time.
Focused attention: reviewers begin with highlighted evidence and likely issues rather than searching every page manually.
Reusable checks: the same review questions are applied consistently.
Shared work: one person can investigate evidence while another reviews the policy interpretation or approves the outcome.
Learning loop: rejected findings and reviewer comments reveal where the detection logic needs to improve.
This is a capacity scenario, not a guaranteed performance claim. Actual throughput and accuracy should be measured against a representative set of claims before the workflow is used for production decisions.
After the event¶
Leaders can group findings by violation type and see which issues appeared most often across distinct claims. Confirmation and rejection patterns help the team identify noisy checks, policy questions that need clarification, and prompt changes worth testing before the next event.