Submissions, claims, bordereaux, bank statements, and remittances arrive in different formats from different partners every day. Intelligent document processing is how insurance teams turn that inflow into structured, usable data without a person retyping it by hand.
Intelligent document processing (IDP) is AI that reads, classifies, and extracts data from insurance documents: typed forms, scanned PDFs, spreadsheets, emails, even handwritten notes, and turns them into structured data your systems can use. Unlike basic OCR, which only converts printed characters into digital text, IDP understands what the document means: which fields matter, how they map to your systems, and what to do when something doesn’t fit the pattern.
The main document types fall into four categories:
Application forms, statements of values, loss runs, and broker attachments. Every broker formats them differently.
FNOL, supporting evidence, laundry lists, and litigation or legal documents.
Periodic reports from MGAs, program partners, and cedants, each on its own template and reporting cadence.
The records that need to be matched against bordereaux to confirm the reported figures and the actual cash position agree.
The volume differs by line and by team. The underlying problem doesn’t: a document arrives in an unpredictable format, and someone has to read it, decide what it says, and re-key it into a system of record.
Three failure patterns show up again and again:
Basic OCR converts printed characters to digital text but has no understanding of what it’s reading. It can’t tell a loss run from an invoice.
Rules-based or template-matching automation breaks the moment a document doesn’t match its trained layout, which is most of the time in a real submissions or claims inbox.
Manual re-keying is what both of the above fall back to, and it’s where the errors get introduced. A mis-keyed field at intake becomes a wrong quote, a misclassified claim, or a bordereaux that doesn’t reconcile.
For more on why this specific problem (unstructured, inconsistent inbound data) is different from ordinary automation, see Tackling Unstructured Data in Insurance and How Insurers Can Turn Unstructured Data Into Actionable Value With AI.
The process breaks into five steps:
The system determines what kind of document it’s looking at (FNOL, SOV, bordereaux, correspondence) regardless of format.
It identifies and pulls the fields that matter for that document type.
Extracted data is checked against rules and existing records for completeness and consistency.
What doesn’t fit the pattern gets flagged for review instead of silently mis-filed.
Corrections your team makes become configuration for your formats and edge cases, so the same exception isn’t reviewed twice. Your data never trains the underlying models.
Document processing is built into each of Brisc’s Analysts, so the output lands where your team already works instead of a separate tool to check.
The Submissions Analyst extracts and structures data from broker submissions in any format (emails, PDFs, spreadsheets, SOVs, even handwritten notes) and applies your underwriting appetite to prioritize what to quote first. Helix Underwriting Partners runs it on 2,975+ submissions processed, with roughly 80% less manual work, 1,700+ underwriting hours reclaimed, 99.1% field-level accuracy, and 91% zero-touch.
The Claims Analyst extracts and structures data from FNOL, supporting evidence, laundry lists, and litigation or legal documents as they arrive, at the same 99.1% field-level accuracy confirmed in production.
The Bordereaux Analyst validates every bordereaux row against binder terms the moment a file arrives, normalizes it into a standard schema regardless of the sender’s layout, and regenerates validated output in whatever format the receiving party needs. It covers premium, claims, and cession bordereaux from any program partner, MGA, or cedant, in Excel, CSV, or PDF, in any layout. Exceptions are queued with supporting evidence for review rather than silently passed through.
The Reconciliation Analyst verifies whether the money actually arrived, matching written premium bordereaux against actual cash received in trust accounts and broker remittances, so the reported figures and the real cash position agree. Premium reconciled through Brisc is on track for ~$1B a year at our current run rate, for MGAs, reinsurers, and specialty carriers across North America, the UK, Bermuda, the Caribbean, and Latin America. In one case, a Bermuda-based specialty reinsurer used this kind of check to audit four years of bordereaux, more than 100,000 premium records, and found a 12% uplift in true profitability that had been hiding in the gap between what was reported and what was actually collected.
For more on how document processing fits into Brisc’s platform as a whole, see the platform overview.
Field-level validation, completeness checks, and consistency verification catch errors before they compound into a wrong quote, a misclassified claim, or a bordereaux that doesn’t reconcile. A full audit trail documents every extraction and exception, which matters for both internal QA and regulatory review.
Document intake stops competing for underwriter, adjuster, and credit-control time. Teams redirect that time to the judgment calls that actually need a person: risk assessment, exception review, relationship management.
Once accuracy and control are in place, processing time drops from hours or days to minutes. That’s real, but it’s the result of getting the first two right, not the reason to adopt intelligent document processing on its own.
Look at two sets of criteria when comparing options:
A generic document-AI tool has to be taught what a bordereaux is, what “written vs. paid” means, or why a loss run matters to underwriting appetite. Brisc’s Analysts start with that context built in.
AI that reads, classifies, and extracts data from insurance documents (submissions, claims evidence, bordereaux, correspondence) regardless of format, and turns them into structured data your systems can use. It goes beyond OCR by understanding document context, not just converting text.
OCR converts printed characters into digital text with no understanding of meaning. Rules-based automation can route and file documents but breaks when a format doesn’t match its template. Intelligent document processing combines extraction with understanding, adapting to new formats rather than requiring an exact match.
Most run general-purpose OCR/IDP platforms, RPA-based automation, or an insurance-native platform. The first two require insurance-specific rules and exceptions to be configured in manually and re-configured every time a format changes. An insurance-native platform starts with the document taxonomy and terminology already understood.
Submissions and supporting attachments, FNOL, supporting evidence, laundry lists, and litigation or legal documents, premium, claims, and cession bordereaux, bank statements and remittances, certificates of insurance, and correspondence, in PDF, scanned image, spreadsheet, email, or handwritten form.
Brisc’s Analysts typically deploy in 2 to 6 weeks, including configuration for your document formats, underwriting appetite, and claims or bordereaux conventions.
No. Brisc sits between the incoming document and your system of record, delivering structured, validated data into the systems you already use.
No. Brisc’s Analysts (Submissions, Claims, Bordereaux, and Reconciliation) share the same underlying document-processing engine, configured per document type, so one platform covers all four instead of four separate point tools.
30 minutes, no slide deck. Tell us where the volume hurts and we show you the Analyst on it. Bring a bordereau, statement or submission if you have one; you don’t need to.
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