AI submission processing is gaining speed

MGAs overwhelmed by unstructured inboxes are turning to AI to classify, extract, and route submissions at scale. The result: cleaner intake, less rekeying, and faster quote-readiness without sacrificing audit or control.

What changes on day one

AI does not replace underwriting. It eliminates the repetitive work around it.

• Auto-triage: Identify LOB, jurisdiction, carrier, new vs renewal, and appetite fit instantly
• Document handling: OCR, dedupe, and manage versions of ACORDs, SOVs, loss runs
• Data lift: Normalize names, addresses, exposures, limits, and validate externally
• Smart routing: Assign to underwriters by skill, authority, and workload with context

Tools like Expert Intake structure messy submissions and route them with prefilled data, so underwriters get to quoting faster with fewer handoffs.

Track what matters

Early adopters of AI intake see tangible results within weeks—not quarters.

• 60–80% faster triage from inbox to ready-for-review
• 25–40% faster first quote response
• 30–50% more submissions handled per underwriter
• 2–5 point hit ratio increase from faster, better-aligned quotes
• Fewer duplicate records and rekeying errors

Quicker triage enables faster broker feedback, which improves submission quality and raises bind potential.

Use a practical, repeatable pattern

Deploy in four steps, tune by LOB, and scale as results stabilize.

1. Intake and classify: Submissions are auto-tagged by LOB, jurisdiction, and completeness. Missing items are flagged with templated broker requests.
2. Enrich and validate: Entities are matched, fields are prefilled, and appetite and authority rules run before underwriter touch.
3. Route and action: Submissions post to the right queue based on expertise and SLA. Tasks are auto-assigned and tracked via workflow tools.
4. Quote or decline: If in-appetite, data flows to quoting with minimal keystrokes. If not, a clear rationale is sent back to the broker.

This structure supports incremental rollout-start narrow, then expand.

Enforce audit and control

Speed does not mean shortcuts. AI intake must meet compliance standards.

• Track what was extracted, what was inferred, and what was human-reviewed
• Apply confidence thresholds to trigger review vs auto-acceptance
• Use reason codes for deferrals and declines
• Feed outcomes back into models to improve future accuracy

Link these controls to your policy management processes for traceability across the lifecycle.

Start small and grow fast

You do not need a big transformation. You need focus.

• Choose one high-volume line and one broker cohort
• Define SLAs, required fields, and what qualifies as “ready to quote”
• Stand up Expert Intake to normalize and route submissions
• Track three metrics weekly: triage time, touches per submission, hit ratio

Iterate thresholds and queues until metrics stabilize-then scale with confidence.

AI intake gives underwriters their time back-and MGAs their edge.