Context

LexiFlow uses a binary classifier to decide whether incoming insurance claim documents can be auto-approved or must go to human reviewers. Today, all claims above a model score threshold are still manually reviewed, and leadership wants to know when the workflow has enough signal to safely automate a subset of decisions.

Current Performance

The Problem

The operations team can manually review only 22,000 claims per day, and backlog has grown 19% month over month. Auto-approving the highest-confidence claims would reduce cost and latency, but an incorrect approval creates regulatory and financial risk.

Requirements

1. Determine whether the score band above 0.92 has enough signal to move from manual review to automation.
2. Explain which metrics matter most for this decision and why overall AUC is insufficient.
3. Quantify the business tradeoff between false approvals and reduced manual workload.
4. Recommend a rollout plan, threshold policy, and validation approach before full automation.
5. Identify what additional analyses you would run by claim type, region, and document quality.

Constraints

• False auto-approval costs an average of $420 in downstream loss and remediation.
• Manual review costs $4.80 per claim and adds 14 hours of latency.
• Regulators require <0.3% harmful auto-decisions on audited claim categories.
• Some claim segments have sparse labels with a 21-day delay.