Data Validation

OCR output can still contain misreads or inconsistent fields, especially across scanned or lower-quality documents. Data Validation sits between extraction and use, applying consistency checks to output from Insurance Document OCR, Policy Data Extraction, Proposal Form OCR, Motor Policy OCR and Health Policy OCR alike.

Trusting automated extraction without a check is how a single misread field turns into a downstream error somewhere else in the business. Data Validation is what makes it reasonable to treat extracted data as reliable enough to act on, rather than something that still needs a person to double-check every field by hand.

What challenges does Data Validation address?

• OCR misreads on scanned or lower-quality documents
• No consistent check before extracted data is used downstream
• Errors that surface only after they've caused a problem elsewhere
• Difficulty knowing which extracted fields need human review

How does it fit into PolicyIQ | Extract?

Validated data is what reaches a brokerage's or insurer's systems through the OCR API, so downstream tools like PolicyIQ | One work from checked data rather than raw extraction results.

What does Data Validation include?

• Consistency checks across extracted fields
• Flagging of fields that look incomplete or implausible
• A checked-data handoff to the OCR API
• Uniform application across all PolicyIQ | Extract capabilities

What outcomes does Data Validation support?

• More trustworthy extracted data
• Fewer downstream errors caused by bad input
• Clear visibility into what needs human review

Related PolicyIQ | Extract capabilities

• Insurance Document OCR — the extraction step Data Validation checks
• OCR API — how validated data reaches other systems
• Back to the PolicyIQ | Extract overview