Document Processing in Reinsurance
Reinsurers process enormous volumes of documents across every transaction — treaty slips, facultative certificates, premium bordereaux, claims bordereaux, statements of account, loss run reports, catastrophe event notices, and actuarial loss development triangles, the list goes on.
Slow or inaccurate processing of cedent submissions delays settlement cycles and exposes reinsurers to reserve errors that compound across portfolios.
LandingAI transforms documents into highly accurate, verifiable, structured data so teams can reliably automate document-intensive workflows.
Why Agentic Document Extraction for
Reinsurance
Cedent Submission Throughput
Reinsurers receive hundreds of files each month — bordereaux, statements of account, loss run reports, and slip schedules — in inconsistent formats across every cedent, and accurate automated extraction removes the manual normalization bottleneck that delays settlement and accumulation reporting.
Audit-Ready Extraction Traceability
Every field extracted from a treaty wording, facultative certificate, or claims bordereau is grounded to its precise location in the source document, giving compliance and audit teams a complete, verifiable record of how cession data entered downstream systems.
Portfolio Exposure Visibility
Faster, more accurate extraction of risk data from cedent submissions and schedule of values documents gives accumulation management teams a cleaner, more timely view of aggregate exposure across treaty lines and catastrophe programs.
Built for Complex Reinsurance Documents
Intelligent document processing across treaty reinsurance, facultative reinsurance, catastrophe reinsurance, and Lloyd's syndicates and captive programs is extremely difficult due to the sheer diversity of document types, the inconsistent layouts and the domain expertise required. Then add multiple languages, handwriting, photographs, scans and faxes to the complexity.
Accurate parsing of dense tables that span multiple pages and contain merged cells.
Single pipeline for image, slide, document, and spreadsheet file types with 1000+ pages.
Strong recognition of character-based languages, handwriting, checkboxes, stamps and signatures.
Schema-driven field extraction with visual grounding traceable to the original document.
Use cases
Cedent Bordereaux Processing
Extract and normalize premium and claims data from bordereaux submissions, statements of account, and loss run reports across cedents whose layouts and field conventions vary by market and line of business.
Accelerate settlement cycles by eliminating manual bordereau normalization
Reduce analyst hours spent reformatting and reconciling cedent data files
Catch mismatched figures between premium bordereaux and statements of account before they reach the ledger
Treaty Bound and Renewal
Extract terms, conditions, limits, retentions, and endorsements from treaty wordings, slip schedules, cover notes, and facultative certificates to populate contract management and pricing systems.
Shorten treaty onboarding time and accelerate the start of premium recognition
Reduce errors from manual re-keying of coverage terms into underwriting systems
Maintain an auditable record linking every contract term to its source document location
Loss Reserving and Catastrophe Response
Extract structured loss data from claims bordereaux, catastrophe event notices, notice of loss packets, and loss development triangles to support reserve reviews and event accumulation tracking.
Accelerate reserve reviews by delivering structured loss data directly to actuarial workflows
Cut the manual effort required to aggregate and validate claims data across multiple cedents
Reduce reserving errors with consistent, traceable extraction from source loss documents
Trusted for Document-Heavy Reinsurance Workflows
Agentic Document Extraction enables reinsurers to automate document-intensive processes that traditionally require manual review.
Very often a loan officer who gets a borrower a solid preapproval fastest earns the deal and the real estate agent’s referrals. Reconstructing income is the hardest part, and Agentic Document Extraction is the cornerstone to getting it right and traceable. Accurate data upfront means we can get a cleaner loan file to the underwriter and get to CTC faster. Get it wrong and everything downstream gets affected.”
View case study →
Agentic Document Extraction has proven to be both accurate and easy to use. We are building on that foundation to deliver reliable, transparent, and scalable automation that our customers can validate and trust.”
View case study →
Trust is the product. Accuracy alone isn’t enough at enterprise scale—what matters is provenance, traceability, and control. LandingAI gives us confidence that every extracted value can be traced back to its source, audited, and defended. That’s what makes it deployable in regulated, real-world environments.”
View case study →Our Plan Review Agent has a lot of complicated components under the hood: traversing building code knowledge graphs, reasoning across disciplines and sheets, assessing issues informed by historical projects. None of it works if we can’t trust what came off the page. ADE gave us a reliable foundation, so our team could focus on incorporating our team’s expertise into our compliance reasoning system.”
View case study →
ADE has significantly outperformed other document extractors we’ve used. It has helped us build an Agentic RAG answer engine, based on unique healthcare institutional content, to offer instant, validated support to medical professionals at the point of care.”
View case study →
I appreciate its reliability and the fact that they're constantly innovating with new models, which helps us work smarter. The service is essential for handling heavy workloads in financial institutions as it provides the necessary infrastructure for high accuracy and fast throughput. I also find it adaptable to specific use cases because they're always working on new models.”
We use LandingAI's Agentic Document Extraction to build pipelines that turn unstructured text into structured data. First, the NER (Named Entity Recognition) detection has amazing accuracy. Second, the OCR capability is excellent — earlier I had to run a separate PDF extractor for text plus a separate LLM with OCR to summarize images, and now it's one step. Third, the image boundary detection is a standout.”
We ran a structured bake-off: the same five PDFs (ranging from a 12-page slide deck to a 400-page machinery manual) processed through other products and Landing AI's Agentic Document Extraction (ADE). We scored each tool on four criteria: Table fidelity, Figure extraction, Chunk typing, Scale. Landing AI ADE was the only tool that scored well on all four.”
Very often a loan officer who gets a borrower a solid preapproval fastest earns the deal and the real estate agent’s referrals. Reconstructing income is the hardest part, and Agentic Document Extraction is the cornerstone to getting it right and traceable. Accurate data upfront means we can get a cleaner loan file to the underwriter and get to CTC faster. Get it wrong and everything downstream gets affected.”
View case study →Agentic Document Extraction has proven to be both accurate and easy to use. We are building on that foundation to deliver reliable, transparent, and scalable automation that our customers can validate and trust.”
View case study →Trust is the product. Accuracy alone isn’t enough at enterprise scale—what matters is provenance, traceability, and control. LandingAI gives us confidence that every extracted value can be traced back to its source, audited, and defended. That’s what makes it deployable in regulated, real-world environments.”
View case study →Our Plan Review Agent has a lot of complicated components under the hood: traversing building code knowledge graphs, reasoning across disciplines and sheets, assessing issues informed by historical projects. None of it works if we can’t trust what came off the page. ADE gave us a reliable foundation, so our team could focus on incorporating our team’s expertise into our compliance reasoning system.”
View case study →ADE has significantly outperformed other document extractors we’ve used. It has helped us build an Agentic RAG answer engine, based on unique healthcare institutional content, to offer instant, validated support to medical professionals at the point of care.”
View case study →I appreciate its reliability and the fact that they're constantly innovating with new models, which helps us work smarter. The service is essential for handling heavy workloads in financial institutions as it provides the necessary infrastructure for high accuracy and fast throughput. I also find it adaptable to specific use cases because they're always working on new models.”
We use LandingAI's Agentic Document Extraction to build pipelines that turn unstructured text into structured data. First, the NER (Named Entity Recognition) detection has amazing accuracy. Second, the OCR capability is excellent — earlier I had to run a separate PDF extractor for text plus a separate LLM with OCR to summarize images, and now it's one step. Third, the image boundary detection is a standout.”
We ran a structured bake-off: the same five PDFs (ranging from a 12-page slide deck to a 400-page machinery manual) processed through other products and Landing AI's Agentic Document Extraction (ADE). We scored each tool on four criteria: Table fidelity, Figure extraction, Chunk typing, Scale. Landing AI ADE was the only tool that scored well on all four.”
