In delegated authority insurance, bordereaux files are the financial truth. Claims bordereaux tell you what's been paid. Premium bordereaux tell you what's been earned. Collateral reports tell you whether the money behind the program is actually there.
Every month, these files flow between MGAs, carriers, and reinsurers — and every month, most operations teams fight the same battle to make sense of them.
We know this because we've lived it. Before building technology to solve it, we spent years inside insurance operations watching talented analysts lose days to the same manual process: reformatting broker spreadsheets, chasing missing fields, reconciling premium line by line against what the contract says should be there.
The problem isn't that teams lack skill or effort. It's that the tools they've been given were never designed for how bordereaux actually work in practice.
Anyone who's managed a delegated authority program knows this: no two brokers send bordereaux the same way. One sends premium data in a clean Excel file with standardized columns. The next sends a PDF with embedded tables. A third emails a CSV that uses different field names every quarter — sometimes different field names every month.
Claims bordereaux are even worse. Loss records arrive with inconsistent policy references, varying date formats, and reserve figures that may or may not include allocated expenses depending on who compiled the file.
This isn't a technology gap that spreadsheets can solve. It's a structural challenge baked into how the market operates. When you have dozens of programs running simultaneously, each with its own set of brokers and its own reporting cadence, the volume and format variability overwhelms any manual process — no matter how experienced the team.
The obvious cost of manual bordereaux processing is time. But the more consequential cost is what you don't see.
When reconciliation runs behind schedule, cash application stalls. Premiums sit unallocated because the reference codes on the bank statement don't match the bordereaux exactly. Settlement timing slips because nobody can confirm the figures fast enough. And when disputes arise — and they always do — the audit trail is scattered across email threads, annotated spreadsheets, and whatever notes the analyst kept along the way.
We've seen this play out firsthand. In one multi-year reinsurance audit, systemic data quality issues had gone completely undetected through manual review. The reconciliation process was technically happening every month, but nobody could see the pattern because they were too close to the individual line items. When AI-powered processing ran across the full dataset, it surfaced a 12% uplift in true profitability that the finance team didn't know existed.
That wasn't a failure of the people involved. It was a failure of the process they were forced to use.
Not all automation is the same. Generic RPA tools and rule-based extraction engines fail on bordereaux because they can't handle the format variability and insurance-specific terminology that makes this data so challenging. A system that works for accounts payable in manufacturing doesn't know what to do with a claims bordereaux that references ALAE, IBNR, and loss adjustment expenses interchangeably.
Effective bordereaux automation needs insurance domain knowledge built in. It needs to understand that "gross written premium" and "GWP" and "written premium" are the same field. It needs to handle the fact that one broker sends loss dates in MM/DD/YYYY and another sends them in DD-MMM-YY. And it needs to do this across PDFs, Excel, CSV, and email attachments simultaneously — because that's the reality of the data.
When this works properly, the shift is significant. Processing that took weeks compresses to hours. Accuracy moves from the 70-85% range typical of manual review to 97%+. And instead of reconciling after the fact, operations teams can see premium and claims data as it arrives — flagging exceptions in real time rather than discovering them at month-end.
The finance team doesn't just save time. They gain visibility they never had before. They can answer questions like "What's our total reconciled premium by broker for Q3?" in seconds, spot persistent data quality issues by source, and go into audits with documentation that's already complete.
Brisc exists because we spent years looking for a tool that could handle bordereaux the way insurance actually works — and couldn't find one. The platforms built for other industries didn't understand the data. The insurance-specific tools were either too narrow (just OCR, just extraction) or required 12-18 months of implementation before anyone saw value.
So we built an agentic AI platform that processes claims, premium, and collateral bordereaux across any format, normalizes the data automatically, matches records against expected values, and produces complete audit trails — all within weeks of deployment, not months.
Teams that have adopted this approach are processing over 100,000 records in single engagements, cutting labor costs by 59%, and fundamentally changing how they manage financial control across their programs.
The hardest part isn't the technology. It's accepting that the process you've been running for years doesn't have to work this way.