How Can AI Help Reconcile Premium Bordereaux with Accounting Records?

A premium bordereau describes business written under a delegated arrangement. Accounting and settlement records describe how the resulting money is recognised and moved.

Those records should connect, but they do not always line up neatly. References can differ between a coverholder, broker and managing agent. Transactions can be grouped at different levels, and exchange rates, endorsements, cancellations and timing can create legitimate differences.

Reconciliation turns those separate views into an explained position. The aim is not simply to make totals agree, but to understand why every material difference exists and how it should be treated.

Why premium records fail to line up

Delegated authority premium flows can contain several relevant currencies and dates. The premium may be written in an original currency, settled by the coverholder in another and passed through a broker in a third. Accounting records may recognise transactions on a different date from the bordereau reporting period.

Identifiers create another problem. A certificate, policy, transaction or unique market reference may be formatted differently across systems. One bordereau row may correspond to several ledger entries, while one accounting entry may aggregate many bordereau records.

Endorsements, cancellations, return premiums, commissions, taxes and corrections add further movements. A difference can therefore indicate a data error, a timing item, a valid transformation or a genuine financial discrepancy. Good reconciliation distinguishes between them.

The control-led reconciliation process

Established reconciliation starts by defining the authoritative sources and standardising the comparison basis. Teams align identifiers, periods, currencies and transaction types, then calculate control totals such as gross premium, commission, tax and net premium.

Exact matching rules clear records where identifiers and amounts agree. Approved tolerances can handle understood rounding effects, while rules for known timing differences prevent them being confused with errors.

Unmatched and out-of-tolerance items move to an exception queue. Technical accountants and delegated authority analysts investigate the source documents, contact relevant parties and record the reason and resolution.

This process remains the strongest foundation because its rules are explainable and repeatable. Its difficult edge is the group of records that probably relate to one another but lack a consistent identifier or level of detail.

Where AI can resolve ambiguity faster

AI can support the ambiguous part of matching by considering several clues together. It may:

• Normalise differently written references and transaction descriptions.
• Suggest many-to-one or one-to-many matches using amount, date, currency and context.
• Group exceptions that appear to share a cause, such as a missing exchange-rate field.
• Summarise the evidence for a proposed match and link it to source records.
• Detect recurring exceptions that point to an upstream reporting problem.

The suggestion should include a confidence level and the evidence used. A strong similarity is not proof that two financial records are the same transaction, particularly where amounts repeat or references are incomplete.

AI is therefore most useful after deterministic rules have cleared the straightforward population. It helps finance professionals investigate the smaller, harder exception set without weakening the control framework.

Protecting financial control and auditability

The workflow should preserve original values before normalisation. It should record every transformation, exchange rate, tolerance, rule result, AI suggestion, reviewer decision and subsequent adjustment.

Segregation of duties should remain in place. The process that proposes a match should not silently post an entry, allocate cash or approve a write-off. Those actions should follow the organisation's established financial authorities.

Teams should test AI suggestions against previously resolved cases and monitor false matches as well as unresolved items. Repeated manual overrides can indicate that a matching rule, source field or coverholder reporting practice needs to be improved.

Successful reconciliation produces an explained and reviewable position. Faster matching is valuable when it lets experienced teams investigate genuine differences sooner while protecting financial accuracy and control.

Example

A hypothetical MGA submits a multi-currency premium bordereau. Its certificate references include local prefixes that are absent from the managing agent's ledger, and several net premium entries are aggregated by settlement batch.

Deterministic rules clear the records with exact references and agreed totals. An AI-supported matching step normalises the remaining descriptions and proposes links between the aggregated settlement entries and their underlying bordereau rows, showing the amount, date and currency evidence for each suggestion.

A technical accountant validates the proposed matches. One item remains outside tolerance because a different exchange rate was used, so the accountant investigates and resolves it separately rather than forcing the reconciliation to balance.

FAQs

• Can AI automatically post reconciled premium entries?

  Match suggestions and financial posting should be separate controls. Posting, cash allocation, adjustments and write-offs need the organisation's established authorities and approval process.

• How should multi-currency differences be handled?

  Keep original, settlement and reporting currencies distinct, use approved exchange rates and dates, and document tolerances. Differences outside those rules should be investigated rather than automatically cleared.

• What should remain in the reconciliation audit trail?

  Retain source records, original and normalised values, rules and tolerances, exchange rates, proposed matches, confidence, reviewer actions, approvals and any resulting adjustment.