How Can AI Support Regulatory Reporting from Delegated Authority Data?

Delegated authority reporting brings together data received from different parties, systems and territories. Fields may be missing, labels may differ and a value that is valid for one return may not satisfy another.

Lloyd's Delegated Authority Reporting Standards establish core data for risk, premium, regulatory, tax and claims reporting. The Delegated Data Manager also applies validation to support downstream reporting. Those standards help, but the reporting firm remains responsible for deciding which obligations apply and whether a submission is correct.

AI can reduce the manual work of preparing and investigating data. The safest design treats it as an assistant inside a controlled reporting process, with approved rules, reconciliation and human sign-off around it.

Separate reporting rules from AI assistance

Start with an authorised inventory of reports, definitions, deadlines, jurisdictions and owners. Legal, tax, finance and compliance specialists should determine the applicable requirements. Store those decisions as versioned rules or reference data where possible.

AI is better suited to variable inputs. It can propose that a coverholder's field corresponds to a standard field, classify free text, retrieve supporting documents or summarise why a record failed validation. It can also compare a new return with earlier submissions to flag unexpected change.

Keep that probabilistic work separate from deterministic obligations and calculations. A model should not decide that a reporting requirement does not apply simply because evidence is absent. It should expose uncertainty and route the case to an authorised reviewer.

Build a traceable source-to-report flow

Preserve the original bordereau and identify its sender, agreement, reporting period and receipt time. Map it to a canonical model, retaining each transformation and any enrichment source. That creates a consistent base for multiple reports without losing the route back to evidence.

Apply field, cross-field and agreement-level validations. Check permitted values, dates, currencies, totals and dependencies. Reconcile record counts and financial control totals from source through transformation to the report. Differences should be explained, not absorbed by rounding or silent exclusions.

Every reported value should be traceable to source data, an approved derivation or a documented manual adjustment. Version the mapping, validation rules, reference data and model used so the submission can be reproduced later.

Route exceptions by risk

Not every exception needs the same response. Combine the confidence of an AI suggestion with the materiality and reporting impact of the record. A high-value ambiguous classification may need compliance review; a low-impact formatting issue may be handled by operations under an approved procedure.

Give reviewers the original value, proposed output, reason, confidence, applicable rule and comparable earlier decisions. Capture their decision and rationale. Repeated overrides can reveal a poor prompt, a weak mapping or a change in business that needs formal rule maintenance.

Monitor the unresolved population as a control. A report that balances only because uncertain records were excluded is not complete. Show outstanding count, value, age and potential deadline impact to the accountable owner.

Control change and final sign-off

Requirements, reference data and delegated portfolios change. Use effective dates and formal approval for rule updates. Test changes against representative and boundary cases, then compare the new output with the previous version before release.

Before submission, produce an evidence pack covering source populations, completeness, reconciliations, exceptions, manual adjustments, versions and approvals. The responsible person should review material movements and unresolved limitations, not merely confirm that a process completed.

After submission, record queries, corrections and late data. Feed those outcomes into control improvement without overwriting the historic record. Success means fewer avoidable exceptions and faster, more defensible preparation—not removal of accountable judgement.

Example

A reporting team receives premium bordereaux from several coverholders with different occupation and territory descriptions. AI proposes mappings to the required standard classifications and retrieves similar decisions from prior periods.

Most high-confidence, low-impact records pass approved checks. Two material cases have conflicting evidence and enter a compliance queue. The reviewer corrects one classification and requests source clarification for the other. Control totals are reconciled after the change.

The final evidence pack shows source files, mappings, rule versions, reviewed exceptions, adjustments and approval. AI has accelerated preparation, while the reporting decision remains controlled.

FAQs

• Can AI submit regulatory returns automatically?

  It should not submit returns without accountable approval. AI can assist preparation and checks, but authorised people remain responsible for applicability, material judgements and final sign-off.

• Where is AI most useful in the process?

  Useful roles include field mapping, free-text classification, document retrieval, change detection, exception explanation and triage. Approved rules should still govern obligations, calculations and validation.

• What audit evidence should be retained?

  Retain source data, lineage, mappings, rule and model versions, reference data, control totals, exceptions, manual adjustments, reviewer decisions and submission approval.