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Use case

Insurance Submission & Claims Intake

Submissions and FNOL packets arrive as unstructured PDFs. Every one is re-typed before an underwriter or adjuster can even look — and quote turnaround is where brokers lose deals.

What can be automated in insurance submission & claims intake? With current AI and integration tooling: extraction from acord/loss runs/schedules with confidence scores; completeness checks against line-of-business rules; claims document classification and key-fact extraction; routing to the right underwriter/adjuster with summary. What should remain under human control: underwriting judgment, pricing, and appetite decisions; coverage determinations and settlements; anything bearing licensed responsibility. Olyra implements this with confidence scoring, audit logging, and human-review gates matched to risk.

The manual reality

What this work looks like today

Who does it

Submission teams, claims intake units, broker servicing staff.

Typical systems

Email, ACORD forms, loss runs, SOVs, policy admin systems, claims platforms, rating tools.

The cost

Measured before we touch anything: Quote turnaround time · Submissions handled per underwriter · Claims cycle time to first action

What changes

What gets automated — and what stays human

Automated (with logging):

  • Extraction from ACORD/loss runs/schedules with confidence scores
  • Completeness checks against line-of-business rules
  • Claims document classification and key-fact extraction
  • Routing to the right underwriter/adjuster with summary

Stays under human control:

  • Underwriting judgment, pricing, and appetite decisions
  • Coverage determinations and settlements
  • Anything bearing licensed responsibility
Measurement & governance

How value and risk are managed

Measure

What we baseline and track

Quote turnaround time · Submissions handled per underwriter · Claims cycle time to first action · Re-key error rate

Governance

Risks designed for

Licensed judgment never automated; regulated-data handling designed with compliance; per-file audit trail.

Typical implementation path: One line of business piloted on last quarter's submissions; production with exception queues. Implementation follows the Olyra 120: pilot on real historical volume, parallel run, then measured cutover.

Find out what AI can actually do for your operations.

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