Fortune 500 Insurance Agency (anonymous)

A Fortune 500 property and casualty insurer serving 29 million people faced a document processing crisis driven by volume and variability. Roughly 70% of email attachments contained handwritten text or low-quality mobile captures — precisely the input types that defeat rules-based OCR. The legacy system required human review on nearly every document, creating cascading delays. During the pandemic, a surge in evidence of insurability (EOI) forms overwhelmed the workflow, producing a growing backlog that left customers waiting on critical coverage decisions. Investment advisors, pulled into administrative review tasks, had less time for client-facing work — straining relationships and eroding service quality.

The insurer replaced its legacy OCR platform with Hyperscience's intelligent document processing (IDP) solution, built on predictive machine learning models capable of classifying and extracting data from more than 200 document types. Unlike rules-based systems that require explicit programming for each document variation, the ML-based approach learns from processed documents, improving accuracy continuously without manual reconfiguration. The platform was integrated into the existing email and attachment workflow, enabling automatic classification and extraction with minimal human intervention. Critically, the new system dramatically reduced onboarding time for new document types — from a multi-month development and implementation cycle to approximately two weeks — giving the insurer the agility to respond to regulatory changes and policy updates without significant IT overhead.

Document processing time fell from 6.5 minutes per email to 1 minute — an 85% reduction — enabling contact center agents to respond to customers five times faster. The system now processes over 200 document types at above 99% extraction accuracy, meeting the accuracy threshold required for agent adoption. Qualitative outcomes were equally significant: employees shifted time from administrative review to higher-value client interactions, and the ML models continue to improve as more documents are processed.

• 85% reduction in processing time (6.5 min → 1 min per email)
• >99% data extraction accuracy across 200+ document types
• 2 weeks to onboard new document types (down from several months)
• Agents able to respond to customers 5× faster

• Rules-based OCR fails at scale when document variability is high — ML-based IDP is required to handle handwriting and inconsistent image quality in production P&C workflows.
• Accuracy is a prerequisite for adoption: contact center agents will abandon tools that generate frequent errors, negating any efficiency gains.
• Fast document onboarding (days to weeks, not months) is a competitive necessity in insurance, where regulatory changes and policy updates constantly introduce new form types.
• Automating high-volume, low-judgment tasks frees licensed advisors and agents for client-facing work — directly improving retention and service quality.
• Continuous model learning means the ROI of ML-based IDP compounds over time rather than degrading as document types evolve.