How Can AI Support Coverholder Due Diligence?

Appointing a coverholder brings underwriting, operational and reputational exposure into a relationship that the managing agent must oversee at a distance.

The assessment may draw on ownership information, financial statements, licences, key staff, underwriting capability, claims arrangements, controls, business plans and previous performance. Evidence can arrive through questionnaires, supporting documents, meetings and specialist reviews.

The practical difficulty is keeping that evidence complete, current and easy to compare. This matters at initial appointment and again at renewal, because a coverholder's circumstances and the proposed contract can change.

Why coverholder due diligence becomes difficult to maintain

Lloyd's guidance expects managing agents to use robust due diligence and approval processes and to maintain effective oversight throughout the relationship. The assessment therefore extends beyond confirming that documents have been received.

Reviewers need to understand what the evidence says, whether it is current, how it relates to the proposed authority and where different sources disagree. A change that looks administrative in isolation, such as a new key individual or system, may affect underwriting control, claims capability or business continuity.

The work also crosses functions. Delegated authority, underwriting, claims, compliance, finance, legal and information security teams may each review a different part of the evidence. Without a consistent record, important qualifications can become buried in emails or repeated questionnaires.

How due diligence is handled today

Established approaches combine structured questionnaires with documentary evidence, references, interviews and specialist review. Teams use checklists and risk ratings to make the process consistent, then refer material concerns to named decision-makers or a delegated authority committee.

This approach provides valuable challenge and context. An experienced reviewer can distinguish a harmless omission from evidence of a weak control environment. They can also ask follow-up questions that depend on the class of business, territory, authority requested and customer exposure.

Its main limitation is the effort required to read and compare a large evidence set. Renewal reviews can become particularly burdensome when teams must identify what has changed since the previous assessment rather than repeat the whole exercise.

Where AI can strengthen the evidence review

AI can help prepare the evidence for professional review. A controlled workflow can:

• Classify documents against the managing agent's due diligence framework.
• Extract relevant facts and link each one to its source.
• Compare a current submission with the previous review and highlight changes.
• Identify unanswered questions, expired documents and conflicting statements.
• Draft a review summary and targeted follow-up questions.

This can make review more consistent across a portfolio and focus specialist attention on gaps and changes. It can also make the history of a relationship easier to follow when staff or document formats change.

The output remains an aid to review. AI cannot determine the materiality of every issue or decide whether the total risk is acceptable. Those judgements depend on the proposed delegation, the managing agent's appetite and the expertise of accountable reviewers.

Controls for an AI-supported review

Source traceability is the first control. Reviewers should be able to move from any extracted fact or summary statement to the exact document, section and version that supports it.

The workflow should use a defined evidence list and flag uncertainty rather than fill gaps with assumptions. Teams should test it against known cases, including documents with qualifications, inconsistent dates and similar entity names.

Access and retention controls are also important because due diligence files can contain confidential corporate information and personal data. The organisation should understand where the AI service processes that information and whether it is retained or reused.

Finally, the record should show who reviewed each material finding, what follow-up occurred and who approved the decision. This preserves the core of due diligence: a proportionate assessment made by accountable professionals using reliable evidence.

Example

A Lloyd's managing agent is reviewing a hypothetical overseas coverholder at renewal. Since the previous review, the coverholder has changed ownership, appointed a new underwriting director and introduced a different policy administration system.

An AI-supported process compares the current submission with the prior evidence set. It prepares a source-linked summary of the changes, identifies an expired business continuity test and flags that two documents describe the new owner's control structure differently.

The delegated authority manager checks the comparison, and specialists in compliance, underwriting and operational resilience assess the material points. The delegated authority committee then decides whether to renew and what conditions or follow-up actions are appropriate.

FAQs

• Can AI decide whether a coverholder is suitable?

  No. AI can organise evidence and identify gaps, but suitability depends on risk, context and the authority being proposed. The managing agent retains responsibility for the assessment and approval decision.

• Which due diligence documents are suitable for AI review?

  AI can assist with questionnaires, policies, financial information, organisation charts and other narrative or structured evidence. Reviewers must still confirm that documents are authentic, current and relevant.

• How should AI-supported due diligence be evidenced for audit?

  Keep source documents and versions, links from findings to evidence, uncertainty and exception records, reviewer actions, follow-up correspondence and the final approval record.