AI for Insurance Underwriting: Submission Intake and Risk Assessment
The problem: submission packages are not single documents
An insurance submission package is not one document. It is a collection: ACORD applications, schedules of values (SOVs), broker cover notes, loss runs, financial statements, prior policies, supplemental questionnaires, and supporting attachments. A typical commercial lines submission contains 10 to 50 documents across hundreds of pages, in varying formats (PDF, Excel, Word, scanned images).
The underwriter’s job is to synthesize this package into a risk assessment. That means cross-referencing declared values on the application against the SOV, verifying loss history against prior policy terms, checking that financial indicators support the requested limits, and identifying gaps or inconsistencies that require follow-up with the broker.
Today, most of this work is manual. Underwriters open documents side by side, copy figures into spreadsheets, and rely on experience to spot problems. The process is slow, inconsistent across team members, and scales poorly. When submission volume increases, teams either extend turnaround times or increase headcount. Neither option improves accuracy.
The risk is not just inefficiency. It is missed information. A discrepancy between reported revenue on the application and the financial statements may never surface if the underwriter runs out of time. A prior claim buried on page 47 of a loss run may not get flagged. These are not hypothetical scenarios; they are the daily reality of submission intake at scale.
How Parsewise addresses submission intake
Parsewise processes the full submission package as a single unit of work, not document by document. The platform ingests all documents in the package, reasons across them simultaneously, and produces structured outputs that map directly to underwriting workflows.
Full-package ingestion
Upload the entire submission in any combination of formats: PDF, Word, Excel, PowerPoint, images, and scans. The Parsewise Data Engine (PDE) handles complex layouts, multi-column forms, merged cells, and handwritten content through a unified parsing pipeline. There is no need to pre-sort or classify documents before upload. The platform processes over 25,000 pages per run, so even the largest submissions are handled in a single pass.
Cross-document reasoning
This is where Parsewise differs fundamentally from per-document extraction tools. Rather than extracting data from each document in isolation, Parsewise links entities across the entire submission package. It connects the insured name on the ACORD application to the corresponding entries in the SOV, loss runs, and financial statements. It matches declared property values against scheduled amounts. It reconciles revenue figures across the application and the financials.
When the platform finds conflicting data, such as a total insured value on the application that does not match the sum of scheduled values in the SOV, it flags the inconsistency with citations to both sources. This cross-document reasoning replaces the manual side-by-side comparison that consumes a significant portion of underwriting time. See Cross-Document Reasoning for a deeper technical explanation.
Configurable extraction agents
Underwriting teams define what they need extracted using extraction agents. Each agent specifies topics (e.g., “property schedule,” “loss history,” “financial indicators”), dimensions (e.g., location, TIV, occupancy, deductible), and validation rules, all in natural language. Agents can be created conversationally through Navi or programmatically through the API.
Once configured, agents are reusable across submissions. A commercial property underwriting agent can process every new submission without reconfiguration, while still adapting to differences in document structure and format across brokers. No templates or pre-defined schemas are required. See How Extraction Agents Work for details on agent configuration.
Structured risk profiles with traceability
Every extracted value is linked back to its source document, page, and specific location. When the platform populates a risk profile showing that the largest scheduled location has a TIV of $42M based on the SOV, the underwriter can click through to the exact cell in the original document. This source attribution makes the output auditable and defensible, which matters for peer review, authority referrals, and regulatory compliance.
Example inputs and outputs
Inputs
| Document type | Typical format | What Parsewise extracts |
|---|---|---|
| ACORD applications | PDF (often scanned) | Named insured, coverage lines, requested limits, deductibles, effective dates, prior carrier information |
| Schedules of values | Excel, PDF | Location addresses, building values, contents values, business income limits, construction type, occupancy, protection class |
| Loss runs | PDF, Excel | Claim dates, descriptions, paid amounts, reserved amounts, incurred totals, claim status, claimant information |
| Financial statements | PDF, Excel | Revenue, net income, total assets, debt levels, key ratios |
| Broker cover notes | PDF, Word | Requested terms, expiring terms, market conditions, broker commentary |
| Prior policies | Expiring coverage structure, endorsements, exclusions, premium |
Outputs
- Submission summary: Structured overview of the insured, requested coverages, key exposures, and broker notes, consolidated from all documents in the package.
- SOV reconciliation table: Scheduled values aligned and totaled, with discrepancies flagged against application-level declared values.
- Loss history analysis: Standardized loss runs with development factors, frequency and severity trends, and large-loss highlights.
- Inconsistency report: Every conflicting data point across documents, with citations to each source and the specific values in conflict.
- Risk profile: Populated underwriting template with all extracted values, ready for underwriter review and authority referral.
All outputs are exportable as structured data (Excel, JSON via API) for integration into underwriting workbenches, policy administration systems, or downstream analytics.
Customer evidence
Compre Group, a specialist in legacy insurance and reinsurance portfolio acquisitions, uses Parsewise to process heterogeneous document packages spanning loss runs, bordereaux, actuarial reports, and policy documents. The platform standardizes loss runs into consistent formats, reconciles paid, incurred, and reserve movements across sources, and flags anomalies and data gaps. While Compre Group’s primary use case is portfolio acquisition diligence rather than new-business underwriting, the underlying challenge is the same: extracting structured, reconciled data from large, multi-format document packages where cross-document consistency is critical.
Why single-document tools are not enough
Per-document extraction tools (Textract, Reducto, Azure Document Intelligence) are effective at pulling structured data from individual documents. They can extract a table from an SOV or parse fields from an ACORD form. But they do not link entities across documents, detect contradictions between sources, or produce a unified view of the submission.
An underwriter does not need 15 separate extraction results. They need one reconciled risk profile that synthesizes the entire package. Building the reconciliation layer on top of per-document APIs requires significant engineering effort and ongoing maintenance as document formats, broker conventions, and business rules change. Parsewise provides this layer natively. For more on this distinction, see Document Packages vs Single Documents.
Deployment and security
Insurance submissions contain sensitive policyholder and financial data. Parsewise is SOC 2 Type II and GDPR compliant, encrypts all data with TLS 1.2+ in transit and AES-256 at rest, and does not train on customer data. Enterprise customers can deploy in VPC or on-premises configurations with regional data residency (EU, US). Full details are available at the Parsewise Trust Center and in the Security & Compliance overview.
Ready to see Parsewise in action? Request a demo or contact sales to discuss your use case.