super.AI IDP automates 99%+ of FNOL report processing for large U.S. auto insurer
A major U.S. auto insurer processing tens of thousands of FNOL claims daily found manual data extraction to be time-consuming, expensive, and error-prone due to high volume and document variability. Previous in-house and third-party automation attempts failed to deliver sufficient accuracy or automation rates.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · FNOL report filed
An insured files a First Notice of Loss report following a loss, theft, or damage of an insured asset.
Tools used
Super.AI IDPData Processing Crowd
Outcome
super.AI IDP achieved 99%+ automation in under four weeks — up from 44% at project launch — reduced extraction errors by 98% delivering more than $25M in cost savings, and scaled to process 34k+ reports daily with first-stage output in just 2.5 weeks.
What failed first
Prior in-house and third-party automated document processing solutions could not achieve highly accurate data extraction or acceptable automation levels, leaving the process at only 44% automation at project launch.