How Do We Build Trust in AI-Driven Data Checks?

When a delegated authority team introduces an AI tool to check bordereaux data, a question quickly follows: how do we know these checks can actually be relied upon?

Teams that have spent years validating bordereaux manually, or through familiar rule-based spreadsheets, are naturally cautious about handing that responsibility to a system whose reasoning is not immediately visible.

That caution is reasonable. Bordereaux validation sits close to underwriting decisions, claims data and regulatory reporting, so getting it wrong carries real operational consequences.

Without a clear basis for trust, organisations tend to do one of two things: reject AI-driven validation outright, or adopt it uncritically and hope it performs as promised. Neither approach manages the risk properly.

This article sets out what actually builds justified trust in AI-driven data checks, and why that trust needs to be earned over time rather than assumed on day one.

Why trust becomes a question when AI enters validation

Manual review and rule-based checks have a long history in delegated authority operations. Reviewers know what the rules are, who wrote them and how exceptions have historically been handled.

AI-driven validation changes that dynamic. The tool may flag an issue that a human reviewer would not have spotted, or fail to flag something a human would have caught. Without visibility into how it reached its conclusion, it is difficult to know which outcome to trust.

This scrutiny is not a sign of resistance to new technology. It reflects the genuine operational stakes involved. Bordereaux data feeds underwriting decisions, claims reserving and regulatory reporting. An oversight team that waves through unreliable checks, whether manual or automated, is failing at its core function.

The right response is not to demand blind confidence in the AI, nor to dismiss it because it is unfamiliar. It is to ask what evidence would justify relying on it, in the same way that any new process or team member would need to demonstrate reliability before being given more responsibility.

How trust has traditionally been established in data validation

Long before AI entered the picture, DA organisations built confidence in their validation processes through several established mechanisms.

Documented validation rules made it clear exactly what was being checked and why. If a bordereau failed a check, the reviewer could point to the specific rule that had been breached.

Sign-off procedures ensured that a named individual took responsibility for confirming that validation had been completed correctly, creating accountability.

Sampling and periodic audit allowed oversight teams to test whether the validation process was working as intended, rather than assuming it continued to function correctly indefinitely.

Audit trails recorded what had been checked, when, and by whom, so that any later query could be traced back to its source.

None of these mechanisms guaranteed perfect accuracy. What they provided was visibility and accountability, which allowed organisations to identify problems early and correct them. Any AI-driven validation process needs to offer the same qualities, not simply better statistical performance.

What builds justified trust in AI-driven checks

Justified trust in an AI validation tool rests on a small number of concrete factors, each of which can be tested rather than taken on faith.

Transparency of flagged exceptions matters as much as the accuracy of the check itself. When the AI flags a bordereau line as an exception, the reviewer should be able to see which field triggered the flag and why it was considered unusual or inconsistent. A tool that simply outputs a list of exceptions with no explanation is much harder to trust or improve.

Evidence from parallel testing gives an organisation a genuine track record before it relies on the tool operationally. Running the AI alongside existing manual or rule-based checks for a defined period, then comparing the results, shows where the AI agrees with human reviewers, where it catches issues they missed and where it misses something they caught.

Consistency of outputs over time and across similar submissions indicates that the tool is applying its logic reliably, rather than producing results that vary unpredictably from one bordereau to the next.

A visible audit trail linking each flag back to the source data allows any exception to be investigated and explained after the fact, in the same way a traditional validation rule could be traced back to its source.

Together, these factors let an organisation explain and verify why it trusts a particular AI validation process, rather than relying on a vendor's claims about accuracy.

Keeping oversight in place as trust grows

Building trust in an AI validation tool is not a one-off decision made at the point of adoption. It is an ongoing process that should continue even after the tool has proven itself.

As confidence grows, the nature of human oversight should shift rather than disappear. Early on, reviewers may check every exception the AI flags, and may also spot-check items it did not flag. Over time, as the evidence base grows, review effort can concentrate more on genuine exceptions and less on verifying routine agreement.

Governance still needs to define who reviews AI-flagged exceptions, how quickly they must be reviewed and what happens when a human reviewer disagrees with the AI's output. A clear resolution process, where disagreements are investigated and the outcome recorded, serves two purposes: it resolves the immediate case, and it builds a record that either supports continued confidence in the tool or highlights where it needs adjustment.

Data patterns and source systems change over time. A coverholder may alter its reporting format, or a new product may introduce data the tool has not previously encountered. Continued spot-checking and exception review remain part of a trustworthy AI-driven validation process, not a sign that the process has failed.

Example

A Lloyd's managing agent is considering an AI tool to validate monthly bordereaux submitted by an overseas MGA writing agricultural risk.

Before switching from its current manual review process, the agent's oversight team runs the AI tool in parallel with manual checks for three reporting cycles, comparing flagged exceptions against what the manual reviewers found independently.

The parallel run shows the AI tool consistently identifies the same exceptions as manual reviewers, plus a small number of additional issues traced back to inconsistent currency formatting.

Having built this evidence base, the oversight team agrees to let the AI tool lead validation going forward, with exceptions still routed to a human reviewer for sign-off.

FAQs

• How long should we run AI-driven checks in parallel with manual review before trusting them?

  There is no fixed rule, but running the tool across several reporting cycles, including cycles that cover unusual or edge-case submissions, gives a far more meaningful evidence base than a single test run. The goal is to see how the tool performs across the range of scenarios it will actually encounter.

• Can an AI validation tool ever be fully trusted without ongoing human review?

  Even a well-evidenced tool benefits from continued spot-checking and exception review. Source systems, coverholder reporting formats and product mixes can all change over time, and ongoing oversight helps catch issues that a one-off evaluation would not reveal.

• What should we do if the AI flags something a human reviewer disagrees with?

  Disagreements should be investigated and recorded through a defined resolution process. This resolves the specific case and also builds a track record that either reinforces confidence in the tool or highlights where its logic needs to be adjusted.