Corebridge Financial

Corebridge Financial, a global insurance provider operating across more than 80 countries and jurisdictions, processes millions of documents annually across its life insurance, individual retirement, and retirement services lines. Life and retirement products are paperwork-intensive by nature — policy applications, beneficiary forms, and annuity enrollment packets frequently arrive handwritten or in degraded condition. The company's existing OCR solution was unable to reliably parse these documents, achieving only around 10% accuracy on handwritten forms. The result was a near-total dependence on manual data keying, creating processing backlogs, prolonged customer response times, and operational costs inconsistent with an enterprise-scale carrier.

Corebridge Financial deployed Hyperscience's Hyperautomation platform, which applies trained predictive machine learning models to extract structured data from imperfect, handwritten, and semi-structured documents that conventional OCR cannot handle reliably. Rather than replacing existing infrastructure outright, the implementation integrated Hyperscience with two RPA platforms already in use — Blue Prism and Automation Anywhere — creating end-to-end automated workflows from document ingestion through data entry. This layered approach allowed Hyperscience's ML-driven intelligent document processing (IDP) layer to handle classification and extraction while the RPA layer executed downstream system writes, eliminating manual touchpoints across the pipeline without requiring a full systems replacement. Hyperscience's user interface was evaluated for ease of adoption prior to rollout, a factor that proved important to the transition.

The deployment produced measurable efficiency gains across Corebridge's document operations:

• Up to 70% reduction in data entry time compared to the prior manual process
• Data accuracy improved substantially from the ~10% OCR baseline, reducing error-driven rework and manual review queues
• Employee adoption was smooth — staff found Hyperscience's interface intuitive, accelerating the transition away from manual keying

Freed from repetitive data entry, employees were redirected toward higher-value tasks. The improvement in accuracy also had downstream quality benefits: fewer errors entering core policy and retirement systems meant less remediation work and more consistent customer records.

• Legacy OCR is not sufficient for high-volume handwritten document processing — ML-based IDP is required to achieve meaningful accuracy at enterprise scale in life and retirement lines.
• Layering IDP over existing RPA investments avoids costly system replacements and accelerates time-to-value; RPA handles workflow execution while ML handles unstructured extraction.
• User adoption should be evaluated during vendor selection, not after — an intuitive interface directly affects how quickly operations staff transition from manual to automated processes.
• Accuracy improvement upstream compounds downstream — reducing extraction errors reduces rework in policy administration, compliance, and customer service systems.