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.
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.
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.
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Frequently asked questions
What did this team achieve with this AI workflow?
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 fir…
What tools did this team use?
Super.AI IDP, Data Processing Crowd.
What results were reported?
FNOL automation rate achieved: 99%+; Extraction error reduction: 98%; Project cost savings: $25M; Documents processed daily: 34k+ (source-reported, not independently verified).
What failed first in this deployment?
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.
How is this claims processing AI workflow structured?
FNOL report filed → IDP data extraction → Flag unclear info for review → Policy coverage validation → Integration with existing systems.