Designing Human Review Workflows for AI Insurance Claims in React Native

An AI system can summarize claim documents, highlight missing information, and recommend a next step. The harder engineering problem is helping a claims professional decide whether that recommendation deserves approval.

A review screen needs more than an “Approve” button. It must show supporting evidence, preserve uncertainty, prevent outdated decisions, and record who authorized an action.

Consumer research supports this emphasis. In Guidewire’s 2026 European Insurance Consumer Survey, 39% of respondents identified the ability to refer an AI decision to a human as a confidence-building factor the highest-ranked option. This is regional survey evidence, not a universal measure of customer preferences.

For React Native teams, the goal is to make human judgment effective and technically enforceable. The same workflow principles apply to React web dashboards.

Define which decisions require review

Different tasks carry different consequences. Extracting an invoice date is not equivalent to denying coverage or authorizing a settlement.

A proposed workflow can distinguish:

These are design recommendations. Applicable requirements depend on jurisdiction, insurance line, and insurer policy.

In the United States, the NAIC emphasizes that insurers remain responsible for decisions made or supported by AI. Its AI Model Bulletin addresses governance and documentation expectations, but is not itself a model law or regulation.

Model the workflow before building the screen

A useful starting point is a server-controlled state machine:

Recording approval and executing payment should be separate events. A failed payment must not appear completed merely because a reviewer approved it.

The backend owns transitions. React Native displays the current state and submits commands; it should not independently decide that a claim is settled.

Present evidence before persuasion

An effective review interface separates four elements:

• Source facts: documents, dates, amounts, and policy references.
• AI interpretation: the proposed explanation or recommendation.
• Unresolved issues: missing documents, contradictions, and extraction failures.
• Human decision: the reviewer’s action and rationale.

Every material statement should link to a relevant source location, such as a document page or structured claim field. A citation is useful only if the referenced evidence actually supports the statement.

Avoid presenting a model-generated confidence score as the probability that a claim is valid. Unless calibrated for that specific task, “95% confident” can create unjustified certainty.

Concrete uncertainty is more actionable: “The invoice total differs from the extracted amount” gives a reviewer something to investigate.

Build a review action component in React Native

The following illustrative component requires a rationale and separates recommendation approval from escalation. It assumes authentication, evidence viewing, and confirmation are handled by the surrounding application.

import React, {useState} from 'react';
import {Button, Text, TextInput, View} from 'react-native';

type Props = {
  canReview: boolean;
  onSubmit: (
    action: 'approve_recommendation' | 'escalate',
    reason: string,
  ) => Promise<void>;
};

export function ReviewActions({canReview, onSubmit}: Props) {
  const [reason, setReason] = useState('');
  const [pending, setPending] = useState(false);
  const [error, setError] = useState('');

  async function submit(
    action: 'approve_recommendation' | 'escalate',
  ) {
    if (!canReview || pending || !reason.trim()) return;

    setPending(true);
    setError('');

    try {
      await onSubmit(action, reason.trim());
    } catch {
      setError('Decision not confirmed. Refresh before retrying.');
    } finally {
      setPending(false);
    }
  }

  const disabled = !canReview || pending || !reason.trim();

  return (
    <View>
      <TextInput
        accessibilityLabel="Review rationale"
        multiline
        value={reason}
        onChangeText={setReason}
        editable={!pending}
      />
      <Button
        title="Approve recommendation"
        disabled={disabled}
        onPress={() => submit('approve_recommendation')}
      />
      <Button
        title="Escalate for further review"
        disabled={disabled}
        onPress={() => submit('escalate')}
      />
      {error ? <Text accessibilityRole="alert">{error}</Text> : null}
    </View>
  );
}

This is a UI pattern, not a complete authorization mechanism. Button disabling cannot prevent forged requests or duplicate backend execution.

Use accessible labels and test with VoiceOver and TalkBack. React Native also provides Android live-region announcements for changing status text; platform-specific behavior needs testing.

Protect decisions against stale evidence and duplicate requests

A submission should identify the claim version, recommendation version, action, rationale, and an idempotency key.

The server should then verify:

1. The authenticated reviewer’s role, assignment, and approval authority.
2. Whether the claim or evidence changed after the review began.
3. Whether the recommendation remains current.
4. Whether the requested transition is permitted.
5. Whether the same command has already been processed.

Reject stale decisions and require renewed review. If a mobile connection drops after submission, retrieve the command’s status before retrying.

For higher-risk actions, a second independent approver may be appropriate. Approval requirements should follow defined rules rather than whatever the model recommends.

Keep untrusted documents away from execution authority

Claim documents can contain misleading text or instructions aimed at an AI system. Treat document content as evidence to inspect, never as authority to change system behavior.

A summarization component generally needs read access. It should not receive unrestricted payment or claim-denial tools.

Guidewire’s security guidance describes human approval gates for consequential actions and persisted workflow state while an agent waits for review. It also recommends authorization checks before tool execution.

Prompt instructions alone are insufficient. Enforce permissions and approval requirements in application code.

What established companies document

The following examples concern published approaches and product capabilities. They do not establish that these organizations implemented the exact React Native workflow above.

GeekyAnts: explicit escalation boundaries

GeekyAnts  insurance AI workflow guidance describes automated intake and status handling alongside human review for disputed decisions, coverage exceptions, and consequential actions. It also discusses task-scoped access and integration with claims systems. This supports an architecture discussion, not a verified React Native claims case study.

Guidewire: approval as persisted workflow state

Guidewire documents pausing a claims agent for approval and resuming from stored state. The practical lesson is to make approval a durable backend event, rather than a temporary frontend dialog.

Shift Technology: routing by claim complexity

Shift Claims describes assessing complexity, prioritizing claims, routing work to handlers, and assisting human review. This illustrates why queues need clear routing reasons and appropriate ownership.

Tractable: surfacing exceptions

Tractable describes reviewing subrogation estimates and photographs against insurer standards, highlighting discrepancies for adjusters. The relevant interface pattern is an actionable exception list linked to evidence.

Measure review quality, not approval speed alone

Useful measures include unsupported statements, reviewer corrections, reopened claims, appeal outcomes, queue age, and unauthorized actions prevented.

A high acceptance rate may reflect good recommendations or reviewers accepting them without sufficient scrutiny. Periodically review samples of accepted recommendations as well as overrides.

Audit records should preserve the evidence version, model and prompt identifiers, recommendation, reviewer identity, rationale, and execution result, under appropriate access and retention controls.

A credible human-review workflow gives claims professionals the evidence, time, and authority to disagree and ensures that the system respects their decision.