Extract structured data from underwriting applications, financial statements, loss runs, and ACORD forms. AI reads any format from any broker or carrier without templates.
Upload any document — PDF, scan, or photo — and get structured data back immediately. No setup, no templates, no waiting.
“We scan 500 loss run PDFs per week from dozens of different carriers. Before OCR, that was two full-time employees doing nothing but data entry.”
“Accuracy on financial statement extraction hit 96% on the first pass. Our underwriters only review exceptions now.”
“Went from a 48-hour turnaround on submission intake to same-day. Brokers noticed.”
Audited controls over a sustained period, not a point-in-time check.
Bank-grade encryption at rest and TLS 1.2+ in transit.
Documents deleted within 24 hours. No copies retained.
Drag and drop files, connect a cloud drive, or set up email auto-forwarding. PDF, JPEG, PNG, TIFF, and digital documents all work.
The AI identifies fields by context and meaning, not fixed coordinates. Named insureds, limits, loss dates, revenue figures, and custom fields come out automatically.
Get structured output in Excel, Google Sheets, CSV, or JSON. Use the REST API to feed data directly into your rating engine or policy admin system.
A commercial underwriter reviewing a new submission might receive a 40-page broker package containing an ACORD application, three years of loss runs from a prior carrier, audited financial statements, and supplemental questionnaires. Manually keying that data into a rating system takes 45 minutes to an hour per submission. Multiply that across 30 submissions per week and the math is straightforward: data entry consumes more underwriter time than actual risk analysis. Insurance OCR solves the extraction half of this problem, and underwriting-specific tools go further by mapping extracted data to the fields that rating engines and policy admin systems expect.
The hardest part of underwriting document extraction is format diversity. Loss runs from Travelers, Hartford, Chubb, and Zurich each use different layouts, column headers, and date formats. Financial statements arrive as audited reports, compiled statements, and tax returns—each with its own structure. Template-based OCR requires a separate configuration for every format variation, which breaks down when you process documents from dozens of carriers and brokers. For a technical walkthrough, our guide on how insurance OCR works explains the difference between template-based and AI-native extraction. Automated underwriting platforms solve this with AI that reads contextually, identifying loss dates, incurred amounts, and reserve figures by their meaning rather than their position on the page.
Lido processes any underwriting document format on the first upload without setup. Batch processing handles submission-season volume spikes when underwriting teams may receive hundreds of new business packages in a single week. The REST API returns structured JSON with field-level confidence scores, so downstream systems can route high-confidence extractions straight through and flag exceptions for human review. For carriers and MGAs evaluating OCR solutions, the combination of format-agnostic extraction, batch throughput, and API-first architecture eliminates the setup and maintenance burden that makes template-based tools impractical at scale. To understand where OCR fits within the broader automation picture, see our overview of what underwriting automation is and how it connects extraction to decisioning.
Security matters because underwriting documents contain financial records, loss histories, and personally identifiable information. Underwriting software platforms that handle this data need SOC 2 Type 2 certification at minimum. Lido provides that along with AES-256 encryption, 24-hour document deletion, and a policy of never training AI on customer data. For organizations that also process insurance claims documents, the same platform handles both workflows under a single security framework.
Underwriting OCR processes applications, ACORD forms, loss runs, financial statements, supplemental questionnaires, prior policy declarations, and broker submission packages. The AI identifies document type automatically and extracts the relevant fields regardless of carrier or broker format.
AI-powered extraction reads each document by context rather than fixed coordinates. A loss run from Travelers looks nothing like one from Chubb, but the AI identifies loss dates, claim amounts, reserve figures, and status fields in both formats without carrier-specific templates or configuration.
Layout-agnostic AI extraction typically achieves 94 to 98 percent field-level accuracy on underwriting documents. Financial statement extraction tends to be at the higher end because numerical tables have consistent structure. Loss runs with handwritten adjuster notes or mixed-format claims histories may require a brief human review on roughly 5 percent of fields.
Yes. Extracted data exports to Excel, Google Sheets, CSV, or JSON. The REST API returns structured JSON with field-level confidence scores, enabling direct integration with policy administration systems, rating engines, and underwriting workbenches. Power Automate connectors are also available for workflow automation.
Lido is SOC 2 Type 2 certified with AES-256 encryption at rest and TLS 1.2+ in transit. Documents are deleted within 24 hours of processing. The platform does not train AI models on customer data. BAA agreements are available for organizations with HIPAA requirements.
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Built on Lido’s OCR engine
Built on Lido’s OCR engine
Built on Lido’s OCR engine