How Do We Spot the Best AI Opportunities in Bordereaux?

Most delegated authority teams know AI could help somewhere in their bordereaux processing. Far fewer have a reliable way of deciding where.

With limited budget and competing priorities, applying AI broadly across every task rarely works well. Some tasks are genuinely well suited to AI. Others depend on judgement and experience that no algorithm should replace.

Getting this wrong in either direction carries a cost. Apply AI too broadly and you risk poor outcomes and wasted investment. Avoid it altogether and you leave genuine efficiency gains on the table while manual effort continues to build up month after month.

This article sets out a practical framework for identifying which bordereaux tasks are strong AI candidates, which should remain human-led, and how to prioritise between the opportunities you find.

The operational challenge of prioritising AI in bordereaux

Bordereaux processing is not one task. It is a sequence of distinct activities: receiving files, interpreting layouts, extracting data, mapping fields, validating entries, resolving exceptions, and ultimately approving the data for use in underwriting, claims or reporting systems.

Each of these activities has different characteristics. Some are repetitive and mechanical. Others require judgement built from years of experience with specific coverholders, classes of business or contract wordings.

The difficulty is that DA teams are rarely given the time or resource to evaluate each activity separately. Faced with dozens of coverholders, inconsistent formats and constant reporting deadlines, it is tempting to either apply AI everywhere or avoid it until a perfect, fully proven solution appears.

Both extremes tend to disappoint. A scattergun approach to AI adoption produces mixed results and can undermine confidence in the technology generally. Waiting indefinitely means the manual effort keeps accumulating, and the operational problem that AI could genuinely help with is left unaddressed.

What is missing in most organisations is not appetite for AI, but a structured way of separating strong opportunities from weak ones.

How organisations have traditionally identified process improvements

Before AI became a realistic option, DA teams looking to improve bordereaux processing typically relied on manual process reviews. Someone familiar with the operation would walk through each step, note where time was being lost, and propose changes: better templates, clearer instructions to coverholders, or additional checks earlier in the process.

Some organisations went further, running time-and-motion studies to quantify exactly how long each task took and where bottlenecks occurred. This produced useful data, but it was labour-intensive and quickly went out of date as coverholders changed their systems or reporting habits.

Others pursued ad hoc automation projects, usually built around rule-based import templates for specific coverholders. These projects delivered results for the coverholders they covered, but the underlying templates required ongoing maintenance and rarely generalised well to new formats.

These traditional approaches share a common limitation. They tend to identify where effort is being spent, but not necessarily which of those tasks are genuinely well suited to automation or AI, as opposed to tasks that are simply time-consuming because they require expert judgement. Effort and suitability are not the same thing, and treating them as interchangeable leads to misdirected investment.

A framework for spotting genuine AI opportunities

A more reliable approach is to assess each bordereaux task against a small number of characteristics.

Volume. Tasks performed frequently, across many bordereaux and many coverholders, offer more opportunity for AI to deliver a cumulative benefit than tasks performed rarely.

Repetitiveness. Tasks that follow a broadly similar pattern each time, even if the input format varies, are stronger candidates than tasks where every instance is genuinely different.

Rule-based versus judgement-based. Tasks that can be described by consistent rules, even complex ones, such as "a policy number should map to this field" or "this figure represents gross written premium", are better suited to AI than tasks that depend on interpreting intent, negotiating with a coverholder, or applying underwriting appetite.

Consequence of error. Tasks where an AI error would be low-impact and easily caught downstream are safer candidates than tasks where an error could affect coverage decisions, claims outcomes or regulatory reporting without being noticed.

Applying these criteria to a typical bordereaux workflow, data extraction and field mapping consistently score well. They are high-volume, repetitive across most coverholders, governed by identifiable rules even when terminology varies, and errors are generally caught during validation before they cause harm.

Exception handling and coverage queries score poorly against the same criteria. Volume is lower, each case tends to be different, resolution depends on judgement rather than fixed rules, and errors can have a direct impact on underwriting or claims outcomes.

Initial data validation, checking for missing fields, invalid dates or obviously incorrect values, sits in between. It is rule-based and repetitive, which makes it a reasonable AI candidate, but the consequences of missed errors mean it should be paired with ongoing human review rather than left entirely unchecked.

Prioritising and governing AI opportunities

Identifying multiple genuine AI opportunities is useful, but most DA teams cannot pursue them all at once. The next step is prioritisation.

A simple way to do this is to weigh each opportunity's operational impact against its implementation effort and risk. High-impact, low-effort opportunities, such as field mapping across a large number of coverholders using inconsistent templates, should generally be addressed first. Lower-impact or higher-risk opportunities can follow once the organisation has direct experience of AI performing well elsewhere in the process.

Throughout this process, it is worth being explicit about what will not change. Tasks that involve underwriting judgement, contractual interpretation or sign-off on exceptions should remain with experienced professionals, regardless of how sophisticated the AI supporting the surrounding process becomes. AI can surface relevant information faster or flag likely issues, but the decision itself stays with people who understand the specific business context.

This distinction matters for governance as much as for accuracy. Oversight frameworks within delegated authority exist precisely because judgement calls need to be traceable to a named, accountable individual. Introducing AI into repetitive, rule-based tasks does not change that requirement for the tasks that remain judgement-based.

Ultimately, the organisations that get the most value from AI in bordereaux processing are not the ones that apply it most broadly. They are the ones that identify, with reasonable precision, which specific tasks genuinely benefit, and which do not.

Example

A Lloyd's managing agent receives monthly bordereaux from twelve coverholders across marine cargo and agricultural risk classes, each submitting data in a different template. The DA oversight team wants to introduce AI but is unsure where to start, given limited budget and competing priorities.

Applying the framework, the team identifies that data extraction and field mapping across the twelve templates is the strongest AI candidate, since it is high-volume, repetitive and rule-based. They decide to keep exception handling and coverage queries with their experienced technicians, and prioritise the mapping opportunity first based on its high operational impact and comparatively low implementation risk.

FAQs

• What makes a bordereaux task a good candidate for AI?

  Strong candidates are high-volume, repetitive and rule-based tasks, such as data extraction and field mapping, where inconsistent formats across coverholders currently cause significant manual effort.

• Which bordereaux tasks should not be automated with AI?

  Tasks requiring underwriting judgement, contractual interpretation or exception sign-off should remain human-led. AI can support these tasks by surfacing relevant information, but the decision should stay with an experienced professional.

• How should we prioritise multiple AI opportunities in bordereaux processing?

  Weigh each opportunity's operational impact against its implementation effort and risk, rather than pursuing every technically feasible option at once. Start with high-impact, lower-risk opportunities such as field mapping.