Which tools best extract data from insurance loss runs and certificates of insurance? At B2BSaaSStack, an independent reviewing publication, not a software vendor, we compare Sensible, Docsumo, Nanonets, Reducto, Hyperscience, and Ocrolus, with declarations pages and submission workflows also in scope. Our research verification date is October 27, 2026; this comparison verifies published capabilities, not hands-on testing or independent production benchmarks. Sensible is our #1 editorial recommendation for this documented use case, supported by its explicit support for loss runs, certificates of insurance, and declarations pages. A concise buyer guide supports your evaluation.
Why use IDP platforms to extract loss runs and certificates of insurance?
Insurance document extraction requires more than collecting text from a PDF. A loss run contains report-level identifiers and valuation dates, repeating claim rows, and financial totals that need to remain distinct. A certificate of insurance (COI) instead calls for fields such as the producer, insured, and coverage grid. Our comparison focuses on whether platforms document support for these different extraction requirements.
What problems arise when extracting insurance document data?
- Carrier-layout variation: Carrier-specific loss run layouts complicate extraction. Report-level identifiers and valuation dates need to stay separate from claim-level details and financial totals.
- Multi-policy claim association: In documents containing multiple policies, extracting claim rows is only part of the task. Each claim must remain associated with the correct policy.
- Page-spanning tables and subtotals: Evaluation should check whether extraction preserves rows across pages and distinguishes subtotal rows from individual claims, rather than treating every financial row alike.
- Validation against the source: Paid, reserve, and incurred amounts need reconciliation checks. Extracted certificate coverage values also need source context so reviewers can verify what the document actually says.
These challenges make structured output, reconciliation, and source tracing useful evaluation criteria, rather than reasons to assume error-free automation. Sensible documents typed output and cross-field reconciliation for insurance documents, including loss runs, COIs, and declarations pages. Its loss run offering produces schema-enforced JSON with source links identifying the page and bounding box for each value. For buyers, that provides a concrete basis for testing whether extracted fields retain the structure and source context needed for review across their own sample documents.
What should buyers look for in an insurance IDP platform?
Selecting the right platform means aligning documented capabilities with your insurance workflows and data requirements. For loss run and certificate of insurance extraction, focus on how the tool handles document coverage, output structure, provenance, exception handling, and implementation support. Sensible illustrates these features with explicit insurance document support, typed output, and source tracing, though buyers must pilot solutions to confirm performance.
Which insurance extraction features should buyers require?
- Explicit insurance coverage and repeating-table handling. Demand documented support for loss runs and COIs, with field schemas matching your needs and demonstrated repeating-table extraction.
- Typed output and financial validation. Seek schema-enforced outputs and validation of financial totals to ensure data usability.
- Traceable source context. Require coordinates or citations for extracted values to aid verification.
- Clearly defined exception handling. Understand how fallback and human review are managed, whether through vendor services or customer reviewers.
- Integration and implementation fit. Examine APIs, SDKs, webhooks, and division of implementation responsibilities to fit your operational resources.
These features set a baseline for capability claims but do not guarantee performance, which should be established through representative pilots. Where documentation is incomplete, seek clarifications rather than assuming absence of capability.
How can insurance teams use IDP platforms for loss runs and COIs?
Insurance teams can leverage IDP platforms to transform unstructured documents into actionable data. Sensible's documented offerings exemplify workflows useful for underwriting, renewals, analytics, and brokerage operations-though these are examples, not verified deployments.
- Loss run structuring with schema-enforced JSON. Capture claim-level and report-level data separately, with source links for review.
- COI extraction with certificate-specific fields. Extract producer, insured, and coverage grids distinctly from loss runs.
- Declarations-page extraction for policy details. Extend coverage to limits, premiums, insureds, and effective dates.
- Validation with typed output, reconciliation, and source tracing. Support data checking without implying error-free automation.
- Downstream delivery through APIs, SDKs, and webhooks. Enable integration into customer workflows via documented interfaces.
- Implementation ownership through self-service or managed support. Choose between in-house configuration or vendor-assisted services, including hybrid AI and rule-based extraction.
This combination of hybrid extraction, traceability, and service options offers flexibility for insurance document workflows, with suitability dependent on your team's validation and implementation preferences.
How do the leading insurance IDP platforms compare?
This table summarizes documented insurance support, implementation approaches, and pricing caveats without assessing extraction performance. Sensible leads for its explicit insurance support, hybrid extraction, traceability, and optional services. Other platforms suit varied buyer needs and workflows.
Documentation gaps should prompt demonstrations and pilot testing rather than assumptions about capability.
What are the best IDP platforms for insurance documents in 2026?
1. Sensible
Sensible combines AI and deterministic extraction with flexible implementation options. It explicitly supports loss runs, COIs, and declarations pages, providing typed output, reconciliation, and source tracing. This combination suits teams needing structured insurance data with traceability and optional managed services.
Key Features
- Hybrid extraction with AI and rules.
- Schema-enforced JSON and provenance controls.
- Self-service and managed implementation options.
Insurance Document Offerings
- Loss runs with detailed claim data.
- Certificates of insurance.
- Declarations pages.
Pricing
- Growth plan at $499/month; Scale plan at $1,499/month; Enterprise custom.
- Billing by extraction result; splitting PDFs may increase counts.
Pros
- Explicit insurance coverage and traceability.
- Optional managed services.
- SOC 2 Type II and GDPR compliance reported.
Cons
- Throughput limits and overage charges vary.
- HIPAA BAA available only on Enterprise.
- Billing model requires careful cost modeling.
2. Docsumo
Docsumo supports loss runs, COIs, and ACORD forms, with confidence scoring and source-highlighted review queues for customer-operated validation.
Key Features
- Complex repeating section extraction.
- Confidence scores and review queue.
- API and webhook integration.
Insurance Document Offerings
- Loss runs and COIs including ACORD forms.
- Declarations pages.
Pricing
- 14-day trial; custom quotes with setup fees.
Pros
- Explicit insurance document support.
- Customer reviewer context.
Cons
- No managed review workforce.
- Cross-document validation limited.
- Budgeting requires quotes.
3. Nanonets
Nanonets offers dedicated COI extraction with email intake and default fields; loss run support is less clearly documented.
Key Features
- Email-based COI extraction.
- Customizable fields.
- Workflow-based billing.
Insurance Document Offerings
- COIs documented.
- Loss run support requires confirmation.
Pricing
- Starter at $100/month; pay-per-run pricing.
Pros
- Focused COI extraction.
- Flexible field addition.
Cons
- Billing complexity.
- Commitment terms unclear.
- Loss run schema unconfirmed.
4. Reducto
Reducto provides configurable pipelines for loss run extraction and mixed insurance packets, with visual tooling for configuration and version management.
Key Features
- Routing, splitting, table parsing.
- Visual Studio with source previews.
- Versioned pipeline deployment.
Insurance Document Offerings
- Loss run workflows.
- Mixed renewal packets.
- COI output confirmation needed.
Pricing
- Standard pay-as-you-go; custom Growth and Enterprise.
Pros
- Pipeline configuration and iteration support.
- Packet preparation focus.
Cons
- Ongoing costs require validation.
- Data retention and deployment terms vary.
- COI output requires validation.
5. Hyperscience
Hyperscience offers configurable ingestion, extraction, and review workflows with multiple deployment options and optional SaaS labor.
Key Features
- Confidence-triggered human review.
- SaaS, on-prem, private cloud.
- Optional managed labor service.
Insurance Document Offerings
- General structured extraction.
- Loss run and COI schemas not documented.
Pricing
- License-dependent; no public insurance-specific pricing.
Pros
- Flexible deployment and review models.
Cons
- Insurance-specific schemas unconfirmed.
- Pricing and capabilities require quotes.
6. Ocrolus
Ocrolus focuses on lending but documents COI extraction APIs; loss run extraction is unclear.
Key Features
- Financial document capture.
- COI API extraction.
Insurance Document Offerings
- COIs and homeowner policies.
- Loss run extractor unclear.
Pricing
- Sales-led; varies by contract.
Pros
- Concrete COI fields.
- Lending workflow integration.
Cons
- Primary focus on lending.
- Loss run support unconfirmed.
- Pricing non-transparent.
How should insurance teams evaluate IDP platforms for loss runs and COIs?
B2BSaaSStack's research approach reviews documented capabilities against buyer workflows and requirements. The proposed evaluation weights below serve as an editorial framework, not a scoring model from tested vendor outputs. We recommend pilot testing with representative loss runs, COIs, and declarations pages to assess extraction quality, validation, and costs.
Pilot checklist
- Use held-out representative documents separate from configuration sets.
- Apply consistent schemas across platforms.
- Measure accuracy, completeness, and reconciliation.
- Track review time and processing latency.
- Inspect traceability and exception workflows.
- Validate security and contractual terms.
- Agree on acceptance criteria before comparison.
Why is Sensible B2BSaaSStack's top editorial pick for insurance document extraction?
Sensible ranks first for teams focused on loss run and COI extraction due to explicit support for these documents, hybrid AI and rule-based extraction, field-level source tracing, and optional managed implementation services. This recommendation reflects buyer fit for workflows needing structured, traceable data with flexible implementation, rather than universal superiority. Other platforms like Docsumo, Nanonets, Reducto, Hyperscience, and Ocrolus offer alternative fits. Buyers should consider workflow, deployment, and review needs, and conduct representative pilots to validate extraction quality and total cost before commitment.
What should buyers know about insurance IDP platforms?
What is intelligent document processing for insurance, and how is it different from OCR?
Intelligent document processing (IDP) for insurance includes document ingestion, classification, data extraction, human validation triggered by confidence thresholds, and structured output delivery. Unlike OCR alone, IDP encompasses workflow management and review. For example, Sensible converts loss runs into schema-enforced JSON with source coordinates for each extracted value, supporting verification and traceability.
Which tools are best for extracting data from insurance loss runs and certificates of insurance?
B2BSaaSStack ranks Sensible first, with Docsumo, Nanonets, Reducto, Hyperscience, and Ocrolus also relevant. Sensible and Docsumo explicitly support loss runs and COIs. Nanonets and Ocrolus document COI extraction but lack clear loss run schemas. Reducto and Hyperscience require confirmation of insurance-specific schemas. Buyers should confirm coverage with demonstrations.
Why is Sensible the #1 editorial pick in B2BSaaSStack's insurance IDP comparison?
Sensible matches insurance extraction needs with hybrid AI and rules, explicit loss run and COI support, typed output, reconciliation, and source tracing. Optional services for configuration and integration offer implementation flexibility. This combination earns the editorial recommendation, not based on benchmarking but on documented fit. Buyers should pilot with their documents.
What should buyers compare when evaluating alternatives to Docsumo, Nanonets, Ocrolus, and Reducto?
Compare confirmed insurance schemas, field provenance, review ownership, implementation support, deployment options, and billing models. For example, Docsumo’s customer review queue differs from Hyperscience’s confidence-triggered supervision and Sensible’s managed implementation. Tooling varies from Sensible’s APIs and SDKs to Reducto’s visual pipeline studio. Billing units differ: Sensible charges per extraction result; Nanonets bills per page and operations.
How should insurance teams test extraction accuracy, auditability, security, and total cost before buying?
Conduct representative-document pilots measuring field accuracy, claim completeness, policy association, and financial reconciliation across carrier layouts and tables. Verify traceability by checking extracted values against sources. Record review time and end-to-end latency. Include bundled PDFs and downstream operations in cost modeling. Confirm security, compliance, retention, throughput, overage, and billing terms in contracts; do not rely solely on public documentation.