How Can AI Reconcile Risk, Premium and Claims Bordereaux?

Risk, premium and claims bordereaux describe connected parts of delegated business, but they often arrive as separate files.

A risk record may use a policy number, the premium file a certificate reference and the claims submission a locally generated claim identifier. Corrections and cancellations can arrive in later periods. Even when each bordereau passes its own validation, the combined view can remain incomplete.

Cross-bordereaux reconciliation creates the links needed for portfolio, claims and reporting analysis. AI can help where exact identifiers fail, but controlled matching, financial totals and visible exceptions remain essential.

Separate submissions fragment one business story

The three bordereaux do not always operate at the same level. One risk may have several premium transactions, instalments or adjustments. One policy may cover several locations. A claim may relate to one certificate, a shared policy or an event affecting several risks.

Timing creates further differences. Claims can be reported long after the original risk period. Premium adjustments may be backdated, and a cancellation can reverse an earlier transaction. Currency, accounting period and signed share may also affect the amounts being compared.

A useful reconciliation therefore needs explicit relationship types. A match is not always one record to one record. The data model must support one-to-many and many-to-one links without duplicating amounts.

Exact matching remains the control foundation

Standardise agreement, policy, certificate and claim references while preserving the originals. Align date formats, currencies and reporting periods. Use a canonical data model so equivalent fields have consistent meanings.

Apply deterministic matching first. Exact or governed composite keys produce the clearest evidence and are easier to test. A composite might combine agreement, certificate, insured name and inception date where no single identifier is reliable.

Reconcile populations and financial control totals separately. Record linkage asks whether records belong together. Amount reconciliation asks whether premium or claims movements agree with expected rules and totals. A strong match can still contain a financial difference, and a balanced total can hide unmatched records.

AI can resolve ambiguous links

AI can compare inconsistent names, addresses, descriptions and dates to propose likely relationships. It may identify that an abbreviated insured name and a full legal name refer to the same party, or that a claim narrative describes a location in the risk file.

Each candidate should show confidence, contributing fields and conflicting evidence. The system should retain alternative candidates where ambiguity remains rather than forcing a single link.

Automatic acceptance may be suitable for tested, high-confidence and low-risk cases. Material claims, shared policy limits and weak identifiers need review. Reviewer corrections should feed controlled improvement, without rewriting the original source.

Operate reconciliation through exceptions

Give every record a status such as matched, partially matched, ambiguous, unmatched or superseded. Route exceptions using value, age, downstream impact and confidence. Reviewers need access to the source submissions and matching rationale.

Late files and corrections should trigger versioned reprocessing. If a changed link alters a material portfolio or reporting total, notify the relevant owner. Preserve prior reconciliation states so historic decisions remain reproducible.

Monitor exact-match rates, accepted AI suggestions, reviewer disagreement, unresolved values and time to resolution. Sample accepted matches as well as exceptions. A falling exception count is useful only if the linked data remains accurate and complete.

Example

A hypothetical managing agent receives monthly risk and premium bordereaux from an overseas coverholder. Its claims bordereau uses local claim references and omits the policy number on older records.

Exact rules link most records using certificate references. For the remainder, AI proposes candidates using insured name, loss location, inception period and claim narrative. One large claim has two plausible policies, so it enters specialist review rather than being accepted automatically.

The reviewer confirms the correct policy from the source file. Control totals still show an unmatched premium adjustment, which operations trace to a late cancellation. The final reconciliation records both the links and the outstanding timing difference.

FAQs

• What if the bordereaux use different identifiers?

  Use governed composite keys and reference data before probabilistic matching. AI can propose links from names, dates, locations and descriptions, but ambiguous candidates should remain visible for review.

• Should high-confidence matches be accepted automatically?

  Only where testing supports a defined threshold and the case is sufficiently low risk. Material claims, weak source data and complex relationships normally require human review.

• How are late corrections handled?

  Reprocess affected periods using versioned data and retain the earlier reconciliation state. Notify owners when changed links or amounts affect material reporting or portfolio decisions.