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50% reduced manual UW activity & 25% improvement in data entry by digitally extracting unstructured data

The Client

US Based Personal & Commercial Insurance provider


  • The new business quote process relied on manual processes to develop underwriting risk analysis. Manual activities included:
  • Review of submission attachments including loss runs, location and fleet schedules, application documents, driver lists, etc.
  • Documentation of key profile criteria such as Public Protection Class, classification code, territory code, etc.
  • Entry of submission data into policy admin or rating system.


  • Implementation of Coforge SLICE (Self Learning Intelligent Content Extraction) solution for data ingestion and extraction
  • Implementation of Terrene Labs (TL) Risk Profile solution for third-party underwriting data
  • Integration of SLICE and TL solutions to produce full underwriting risk profiles
  • Full integration of prior company loss reports (provided by prior insurance carriers) into a full underwriting risk profile

Value Delivered

  • 50% reduction in manual Under-wiring activities for each new submission
  • 25% improved accuracy from a reduction in data entry errors
  • Allowed UW to generate more business & time for prospective marketing
  • Enabled insurer to discontinue maintenance of proprietary XML schema in favor of industry standard (ACORD XML)'
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