What Is the Best Approach for Testing AI-Based DA Solutions?

Delegated authority data arrives in inconsistent formats from many coverholders and MGAs, and errors in bordereaux processing can affect premium accounting, claims and regulatory reporting.

When organisations introduce an AI-based solution to help process this data, they need confidence that the tool performs reliably across the full range of real-world variation it will encounter, not just the clean examples used in a vendor demonstration.

Because AI behaviour can vary depending on the data it sees, testing needs to go beyond a single pass/fail check and consider how performance holds up over time and across edge cases.

This article sets out a practical, risk-based method for testing AI-based DA solutions before and after go-live.

Why Testing AI-Based DA Solutions Is Different

Traditional software behaves predictably. Given the same input, a rules-based system will always produce the same output, so testing typically focuses on confirming that defined rules have been implemented correctly.

AI-based tools behave differently. Their output can vary depending on the range and quality of data they encounter, particularly when that data comes from many coverholders using different formats, terminology and levels of data quality.

This means a test that only checks a handful of clean sample bordereaux tells you very little about how the tool will perform against the full variety of submissions it will see in production. Testing needs to account for this variability directly, rather than assuming that good performance on a demo dataset will translate into good performance across an entire bordereaux portfolio.

Traditional Approaches to Testing DA Systems

Organisations have long-established methods for testing new bordereaux or data processing systems.

Functional testing confirms that a system performs specific tasks correctly, such as importing a file or applying a validation rule. User acceptance testing (UAT) involves operational staff checking that the system behaves as expected using a defined set of scenarios. Manual sample checking involves reviewing a subset of processed records against source documents to confirm accuracy.

These approaches remain useful and should not be discarded. However, they were designed for systems whose behaviour is fixed once configured. Applied on their own to an AI-based tool, they risk giving false confidence: a tool can pass a small, well-defined set of UAT scenarios and still struggle with the messier, more varied data it encounters once live. Testing an AI-based solution needs to extend beyond these traditional checks.

A Risk-Based Approach to Testing AI-Based DA Solutions

A risk-based testing method addresses this gap in four practical steps.

First, select representative test data. This should be drawn from real historical bordereaux, not synthetic or vendor-supplied samples, and should deliberately include known edge cases and past exceptions, such as missing references, unusual currency formats or non-standard layouts.

Second, define acceptance thresholds in advance. Operational and oversight stakeholders should agree, before testing begins, what level of accuracy, exception rate or manual intervention is acceptable. Setting these criteria after seeing results risks the thresholds being adjusted to fit whatever the tool achieves, rather than what the business actually requires.

Third, run structured UAT with oversight involvement, using the agreed test data and thresholds, and documenting where the tool performs well and where it does not.

Fourth, use parallel running before full cutover. Operating the AI-based solution alongside the existing process for a defined period, typically covering at least one full reporting cycle, allows discrepancies to be identified and investigated without disrupting live operations. Only once performance has been demonstrated consistently should full reliance replace the existing process.

Operational Considerations for Ongoing Monitoring

Testing does not end once a solution goes live.

Coverholder data patterns change over time as new coverholders are onboarded, products evolve or existing coverholders alter their reporting formats. Ongoing monitoring is needed to catch any resulting drift in performance before it affects downstream reporting.

This typically involves periodic re-testing against fresh samples, a clear escalation process for exceptions identified during live processing, and defined responsibility for reviewing performance at agreed intervals, such as quarterly.

Throughout testing and beyond, human sign-off remains essential. AI can reduce the repetitive burden of interpreting inconsistent bordereaux, but decisions about whether a solution is fit for purpose, and ongoing oversight of its performance, remain the responsibility of experienced DA professionals.

Example

A London managing agent is piloting an AI-based tool to process monthly bordereaux from a portfolio of agricultural risk coverholders operating in several territories.

Before relying on the tool for live reporting, the operations team designs a three-month testing programme using twelve months of historical bordereaux, including several known problem cases such as inconsistent currency formatting and missing policy references. They agree acceptance thresholds with the oversight team, then run the AI tool in parallel with their existing manual process before deciding whether to fully switch over.

The parallel run reveals that the AI tool handles standard bordereaux reliably but initially misclassifies a specific currency format used by one coverholder. The team addresses this before full cutover, and agrees a quarterly review process to keep monitoring performance as new coverholders are added to the binder.

FAQs

• How long should testing an AI-based DA solution take?

  Duration depends on data volume and complexity, but a period covering at least one full reporting cycle, often a month or more, plus a parallel run, is generally advisable rather than a single one-off test.

• Who should be involved in signing off an AI-based DA solution for go-live?

  Sign-off should involve both operational teams and oversight or compliance stakeholders, not just IT or the vendor, because acceptance criteria need to reflect both data quality and governance requirements.

• Does testing stop once the AI solution goes live?

  No. Ongoing monitoring is necessary because data patterns from coverholders can change over time, and periodic re-testing helps catch performance drift early.