How Do We Establish Trust in AI DA Outputs?

Delegated authority oversight teams remain accountable for the accuracy of bordereaux data even when a coverholder or MGA has performed the initial processing.

Introducing AI into that chain adds a further layer that must itself be trusted before it can support decisions such as premium booking, claims reserving or regulatory reporting.

Without a clear basis for trust, oversight teams tend to do one of two things: reject AI tools outright, or use them without adequate scrutiny. Both carry operational risk.

Establishing trust is therefore not a one-off decision made at the point of purchase. It is an ongoing governance question with a practical answer.

Why trust in AI outputs is an operational question

When a coverholder submits a bordereau, the managing agent or insurer remains responsible for the accuracy of what is booked, reserved or reported downstream. That accountability does not shift simply because an AI tool, rather than a person, performed the initial cleaning, mapping or validation.

This means AI outputs cannot be accepted at face value. Before an oversight team relies on an AI-generated result, whether that is a cleaned bordereau, a set of mapped fields or a list of flagged exceptions, it needs a defensible basis for believing that result is correct.

That basis is what we mean by trust in this context: documented, repeatable evidence that the AI's output is accurate enough, often enough, and that the cases where it is not are reliably surfaced for human review.

How organisations traditionally establish trust in data processes

Delegated authority teams already have a toolkit for establishing trust in any new data process, AI or otherwise.

Common techniques include:

• Sample-based quality assurance, checking a proportion of records against source documents.
• Reconciliation, comparing totals and key fields against independent figures such as premium accounting records.
• Peer review, where a second person checks the work of the first before it is signed off.
• Audit sign-off, where a named individual formally accepts responsibility for a batch of processed data.

These approaches were developed long before AI entered bordereaux processing, and they remain the foundation for building trust in AI outputs. The question is not whether to use them, but how to adapt them to a process where a machine, rather than a person, has done the initial interpretation.

Where AI changes what trust-building requires

AI-generated outputs introduce a few characteristics that traditional QA was not originally designed to address, so the assurance process needs some additions.

Useful mechanisms include:

• Confidence scoring, so reviewers know which outputs the AI is less certain about.
• Exception flagging, where the AI identifies records that fall outside expected patterns and routes them for human review rather than processing them silently.
• Explainability, showing why the AI mapped or classified a field a certain way, not just the final result.
• Ongoing performance monitoring, tracking accuracy over time rather than assuming a single test result holds indefinitely.

These additions extend traditional assurance rather than replace it. Sampling and reconciliation still matter, but they are now targeted more efficiently using confidence scores and exception flags to focus human attention where it is most needed.

Building an assurance process around AI-assisted DA work

In practice, embedding trust into day-to-day operations means defining a few things clearly before relying on AI outputs.

Organisations should establish:

• Who reviews AI-flagged exceptions, and within what timeframe.
• How disagreements between the AI's output and a reviewer's judgement are resolved and recorded.
• What audit trail is captured, including the data the AI worked from, the decision it made and the confidence level, not just the final figure.
• How often accuracy is re-tested, particularly after a coverholder changes its reporting format or the AI tool is updated.

Sign-off authority should remain with experienced DA professionals throughout. AI can reduce the volume of routine interpretation work, but the decision to accept a batch of processed bordereaux as fit for use is, and should remain, a human one.

Example

A Lloyd's managing agent pilots an AI tool to process monthly bordereaux from an agricultural risk coverholder.

Before relying on the tool's output for premium booking, the oversight team runs the AI in parallel with manual processing for three months, comparing results and reviewing every flagged exception.

After three months of parallel running and a defined exception review process, the oversight team gains sufficient evidence to trust the AI's routine outputs, while retaining manual review for flagged exceptions and unusual submissions going forward.

FAQs

• How long does it take to establish trust in an AI tool for DA processing?

  Timeframes vary depending on data volume and complexity, but a period of parallel running or sampled validation, often several months, is typical before an organisation moves to full reliance on AI outputs.

• Does using AI reduce the need for audit and oversight?

  No. AI changes what needs to be scrutinised rather than removing the need for scrutiny. Oversight and audit remain essential, though they can be focused more efficiently using confidence scores and exception flags.

• What evidence should we ask an AI vendor to provide?

  Ask for accuracy benchmarks tested against your own data, explainability features, details of exception-handling logic and audit trail capability, rather than relying on general accuracy claims.