How Can AI Support Delegated Authority Audit Planning?

Delegated authority teams rarely lack things they could audit. The harder question is where assurance effort will have the greatest value.

A managing agent may oversee many binders, coverholders and delegated claims administrators. Each relationship generates different signals: bordereaux timeliness, claims performance, complaints, breaches, outstanding actions, staff changes and shifts in premium or exposure. Reviewing every signal manually before each planning cycle is slow, and simple thresholds can miss combinations that matter.

AI can help organise that evidence and surface unusual patterns. Its role is to improve the planning conversation, not to replace it. Lloyd's guidance treats audit as part of risk-based oversight and keeps accountability with the managing agent. That means a model's ranking must remain explainable, challengeable and subordinate to professional judgement.

Start with the audit decision

Define exactly what the system is helping people decide. That might be which entities enter the annual audit plan, which binder needs a targeted scope, or whether a recent event warrants an earlier review. Mixing those decisions produces a score that is difficult to interpret.

Choose the unit of analysis too. A coverholder may operate several binders with very different risk profiles, while a delegated claims administrator may serve several principals. A single organisation-level score can hide the relationship that actually needs attention.

Record the population, planning horizon, risk appetite and minimum mandatory reviews before using AI. Regulatory commitments, contractual rights, previous findings or a material incident may require action regardless of a score. Those constraints belong in the decision process, not as an afterthought.

Build an evidence-led risk view

Useful evidence can include late or incomplete bordereaux, validation exceptions, rapid premium growth, unusual claims development, complaint trends, authority breaches, overdue actions and results from previous audits. Qualitative intelligence from relationship owners may add context that structured data misses.

AI can reconcile names and identifiers, summarise case notes, detect changes and compare patterns across the audit universe. A transparent method might assign approved weights to indicators and show the contribution of each one. A more complex model may find subtler relationships, but only if the team can still explain why an item was surfaced.

Data quality matters as much as model quality. Missing information must not quietly become a low-risk signal. A delegate with weak reporting may require more scrutiny, not less. Teams should expose data coverage alongside any ranking and distinguish “no issue observed” from “insufficient evidence”.

Keep judgement and challenge in the loop

The output should be a recommendation pack, not an automatic instruction. For each proposed priority, reviewers need the underlying indicators, source dates, comparisons and known limitations. An audit lead can then add intelligence about organisational change, market events or remediation that the data does not capture.

Use a documented approval forum with representatives from delegated authority, audit and relevant risk or compliance functions. Record accepted recommendations, overrides and reasons. Overrides are valuable evidence: repeated disagreement may show that an indicator is poorly calibrated or that important information is missing.

Access controls are also essential because the evidence may contain commercially sensitive or personal information. Limit processing to a clear purpose, minimise unnecessary fields and retain a usable audit trail of source, transformation, model version and reviewer action.

Measure whether prioritisation improves assurance

A high score is not proof that the method works. Compare recommendations with completed audit findings, severity, recurring issues and unanticipated events. Ask whether the approach identified material concerns earlier, reduced planning effort and broadened coverage without concentrating repeatedly on large delegates.

Review false positives and false negatives. Volume-heavy businesses may appear riskier simply because they generate more exceptions, while smaller relationships can be overlooked. Normalised measures, peer groups and minimum review rules can reduce that bias.

Refresh the assessment when material events occur and at an agreed planning interval. Changes to products, territories, claims authority, ownership or reporting quality may be more important than the calendar. Recalibrate indicators through governance, document the change and preserve earlier versions so decisions remain reproducible.

Example

A managing agent must choose eight audits from a population of more than 100 delegated relationships. Its team combines prior findings, overdue actions, bordereaux timeliness, premium change, claims leakage indicators and complaint trends. The system flags twelve relationships and shows the evidence behind each result.

The audit committee accepts six recommendations, adds one relationship after a recent ownership change and selects another because a new claims authority has not yet been tested. It records why two model recommendations were deferred. After the audits, the team compares significant findings with the original indicators and adjusts one measure that had over-weighted transaction volume.

AI has not made the assurance decision. It has made the evidence easier to compare and the committee's judgement easier to document.

FAQs

• Can AI decide which coverholders must be audited?

  It can recommend priorities, but accountable owners should approve the audit plan and scope. Mandatory reviews, contractual obligations, qualitative intelligence and recent events may override a model's ranking.

• What data is useful for audit planning?

  Useful inputs may include bordereaux timeliness and quality, premium and exposure movements, claims and complaints outcomes, authority breaches, prior findings, overdue actions and relationship-manager intelligence. Data coverage and provenance should be visible.

• How often should an AI-supported audit ranking be reviewed?

  Review it at the normal planning interval and whenever a material event changes the risk picture. The frequency should reflect the firm's risk appetite, data refresh cycle and the nature of each delegated relationship.