How to Automate Bordereaux Processing with AI

Bordereaux processing is one of the most repetitive and error prone jobs in delegated insurance, which makes it a natural fit for automation. A bordereau is the periodic spreadsheet that a coverholder, managing general agent (MGA), or delegated authority sends to its carrier or reinsurer, listing the risks written, the premium booked, or the claims paid over a period. To automate bordereaux processing with AI means using an external AI layer to read, map, validate, and reconcile those files as they arrive, so clean and structured data reaches the system of record without a person keying and checking every row.

Why bordereaux are so hard to process

The difficulty is not the concept, it is the variety. Every coverholder tends to send its own template, so column names, ordering, date formats, currency conventions, and even the meaning of a field drift from one source to the next. One binder labels a column "Sum Insured", another calls the same thing "TSI" or "Limit", and a third splits it across two columns. Files arrive as spreadsheets, exported reports, or scanned attachments on a monthly or quarterly cadence, often late and often revised. Someone then has to open each one, understand its layout, key or paste the values into the delegated authority system, and reconcile premium and claims figures that rarely tie out on the first pass.

Done by hand this is slow, and it does not scale. Adding coverholders means adding headcount, and every manual step is a place where a transcription error or a missed exception can distort reporting to capacity providers and reinsurers. This is precisely the kind of high volume, low variance work where a document reading pipeline earns its place.

The scale behind the problem

Delegated authority is not a niche corner of the market. Lloyd's of London, the world's leading insurance and reinsurance marketplace, has described business written under delegated authority as a large part of its market, on the order of a third of premium. Whatever the exact figure in a given year, the direction is clear. A very large share of specialty and commercial risk is written through binders, and every binder produces bordereaux on a recurring cycle. Multiply that across a book of coverholders and the reporting burden becomes structural rather than occasional.

The reason it stays manual is rarely appetite for change. Boston Consulting Group has found that roughly 70% of insurers fail to execute on innovation because of IT limitations, which is exactly why an approach that leaves the core untouched matters. The goal is to remove the manual step without asking the operation to migrate or rebuild the systems it already trusts.

What automating with AI actually means

Automation here is a pipeline, not a single model, and it runs in a few clear stages.

Intake. The layer receives bordereaux from wherever they land, whether that is a mailbox, a shared folder, or an upload, and it recognizes each file as a bordereau to be processed.

Intelligent reading and column mapping. Machine learning reads the file and maps its columns to a common target schema, even for layouts it has never seen before. "TSI", "Sum Insured", and "Limit" all resolve to the same field, dates and currencies are normalized, and every mapping carries a confidence score.

Validation. The system checks each row against rules and reference data. Missing mandatory fields, out of range values, invalid identifiers, and totals that do not add up are flagged rather than silently passed through.

Reconciliation. Premium and claims figures are reconciled against expected values and prior periods, so discrepancies surface as specific, reviewable items instead of a vague sense that the numbers are off.

Exception routing. High confidence rows flow straight through to the system of record. Only ambiguous mappings, failed validations, and reconciliation breaks are escalated to a person, with the reason attached. The machine acts when it is sure and hands off when it is not.

Write back. Clean, structured, mapped data is written to the delegated authority system or data warehouse, ready for reporting to carriers, capacity providers, and reinsurers.

An external layer, not a core replacement

This is the defining point. WIR is an external AI layer that sits on top of existing systems and never replaces the core. The bordereaux layer reads the files, structures them, and writes clean data back, but the system of record, the delegated authority platform, and the underwriting manual all stay exactly as they are. There is no migration, no rip and replace, and no load on the operation's own IT team. The intelligence lives in front of the core, not inside it.

The same document reading approach that powers bordereaux processing has been validated in a proof of concept with a global insurer in the transport line, one of the most document heavy classes an operation can touch. That is the pattern generalized. If the layer can structure the messiest transport paperwork, a monthly bordereau is well within reach.

Built for Brazilian regulation

For operations regulated in Brazil, the same layer is designed to run inside local expectations. Every mapping, validation, and exception is logged and explainable, which supports the auditability that SUSEP oversight assumes. Personal and business data flowing through the pipeline is handled in line with the LGPD, so automating the reporting cycle does not create a new compliance gap. The layer speeds the work up while keeping a clear, reviewable trail behind every field it touches.

How to measure whether it is working

Bordereaux automation is worth doing only if you can see the gain, so instrument it. Track the straight through processing rate, meaning the share of rows that clear the pipeline without human touch, alongside the exception rate, the cycle time from file received to data booked, and a simple data quality measure such as validation failures caught before reporting. A healthy program pushes straight through processing up and cycle time down while catching more errors earlier, not fewer.

Getting started

Begin with your highest volume coverholders, the ones whose bordereaux consume the most hours and generate the most reconciliation pain. Point the external layer at their historical files so it learns the layouts, set confidence thresholds conservatively so that anything uncertain routes to a person, and expand coverage as trust builds. Because the layer sits on top of your existing systems, you can prove the value on one binder before scaling across the book, without touching the core at all.