How Can AI Help Detect Underwriting Authority Breaches?

A managing agent needs to know whether business written by a coverholder remains within the authority it has granted.

That sounds like a straightforward comparison. In practice, the binding authority may contain class, territory, date, premium, sum-insured, referral and exclusion terms across a main contract and later endorsements. The bordereau may describe the same concepts using different labels or incomplete detail.

Rule-based validation remains the control foundation. AI becomes useful where contracts and reported records require interpretation at scale. Its output should identify potential exceptions with evidence, leaving qualified people to determine whether a breach has occurred.

Authority monitoring spans contracts and data

Authority terms and transaction evidence live in different forms. A contract may permit a class only within named territories, impose a maximum line size, require referral above a threshold or exclude particular activities. An endorsement may change one condition from a specific effective date.

The corresponding bordereau may contain a local occupation description, a group-level limit or an address that does not map cleanly to an approved territory. Missing fields can create uncertainty without proving non-compliance.

Monitoring must therefore apply the correct terms to the correct transaction and period. A valid risk under one contract version may be outside authority under another. Weak version control creates false alerts and can also allow genuine exceptions to pass unnoticed.

Rule libraries strengthen established controls

Traditional monitoring uses contract reviews, authority matrices, bordereaux checks, referrals and sample-based audit. These controls provide an explicit connection between agreed authority and oversight activity.

Convert approved terms into a governed rule library where they can be tested reliably. Rules might check inception dates, class codes, territories, premium or sum-insured limits and the presence of required referral evidence. Each rule should link to its source clause, endorsement, effective date and owner.

Exact rules work best when the reported data is standardised. Exceptions should already be classified by reason and materiality, with a clear route to the underwriter, delegated authority team or compliance function.

AI helps interpret variable evidence

AI can propose candidate authority rules from contract documents, but an authorised specialist must approve them before use. It can also classify free-text risk descriptions, resolve territory names and compare records with referral correspondence or endorsement wording.

Confidence should be visible. A clear territory mismatch under an approved rule is different from an uncertain occupation classification. The first may require urgent review, while the second may need source clarification before anyone describes it as a suspected breach.

AI can also group recurring exceptions and summarise the evidence for reviewers. This reduces repetitive investigation while retaining the original transaction, applied rule, relevant clause and model explanation.

Alerts require controlled investigation

Design the review queue using confidence, financial materiality, customer impact and recurrence. Reviewers need authority to accept a valid referral, correct the data, confirm a breach or escalate ambiguity for contractual interpretation.

Record the investigation and rationale. If a breach is confirmed, the response may include impact assessment, correction, notification, remediation or changes to monitoring. The appropriate response depends on the agreement, governance and jurisdiction, so it should not be automated from the alert alone.

Monitor the control through false-positive rates, missed issues, unresolved age and repeat causes. Re-test rules when contracts, products or reporting formats change. Legal interpretation should remain with qualified advisers where wording is disputed.

Example

A hypothetical marine coverholder reports two risks using local trade descriptions. One appears to exceed a sum-insured limit, while the other may fall outside the permitted occupation classes.

The control applies the correct endorsement and confirms that the first risk was referred and approved before inception. AI maps the second description to two possible classes with medium confidence and presents the source fields and relevant clause.

The delegated authority analyst asks the coverholder for supporting information. The class underwriter then confirms that the risk falls outside the intended authority and follows the firm's escalation process. The first alert is closed with referral evidence; the second is recorded as a confirmed exception only after review.

FAQs

• Which authority terms can be monitored from bordereaux?

  Depending on available data, controls may monitor classes, territories, inception dates, limits, referral conditions and exclusions. Some terms require documents or contextual evidence beyond the bordereau.

• Can AI interpret a binding authority contract?

  AI can extract candidate terms and locate relevant clauses, but authorised specialists should approve the operational rules. Ambiguous or disputed wording may require legal review.

• What should happen after a suspected breach is found?

  Check the applicable contract version, endorsements, referrals and source data. If confirmed, assess impact and follow the firm's escalation, notification, remediation and record-keeping procedures.