How can AI map bordereaux fields to standard schemas?

Every managing agent, insurer or MGA that receives bordereaux from multiple coverholders faces the same underlying task: translating whatever format arrives into a consistent, standard schema that internal systems and reports can rely on.

A coverholder might label a field "Policy Inception", another "Cover Start Date", and a third simply "Start". All three may mean exactly the same thing, but nothing in the data tells a computer that automatically.

Field mapping is the process of resolving these differences: deciding which incoming field corresponds to which field in your standard schema, so that data from dozens of sources can be processed, validated and reported on consistently.

This article explains how that mapping has traditionally been done, and how AI-based approaches are changing what is operationally achievable.

Operational Context

Delegated authority arrangements routinely involve dozens, sometimes hundreds, of coverholders and MGAs, each submitting bordereaux on their own schedule and in their own format.

Before that data can be used for premium accounting, exposure management or regulatory reporting, every field in every submission has to be understood and mapped onto a single, standard schema. This might be an internal target template or a market-wide structure such as Lloyd's Core Data Record.

The scale of the problem is what makes it operationally significant. A single coverholder's spreadsheet might contain thirty or forty fields. Multiply that across a large delegated authority book, and the volume of mapping decisions required every reporting cycle becomes substantial.

When mapping is slow or inconsistent, the consequences are felt downstream: delayed bordereaux processing, reporting errors, and oversight teams working from data they cannot fully trust.

Traditional Approaches

The conventional approach to field mapping relies on two things: manual review by data analysts, and static rules or lookup tables that record known equivalences between source fields and target schema fields.

When a new coverholder is onboarded, an analyst typically works through their bordereaux template line by line, deciding what each column represents and building a mapping rule for it. Once built, that rule can usually be reused for future submissions from the same coverholder, provided their format doesn't change.

This approach works well when relationships are stable and volumes are manageable. For a delegated authority operation with a handful of long-standing coverholders using consistent templates, manual mapping tables remain a perfectly adequate solution, requiring no additional technology investment.

The difficulty arises at scale. Every new coverholder, every format change, and every non-standard field requires manual attention. Lookup tables accumulate over time and become harder to maintain. Analysts spend a disproportionate amount of time on interpretation rather than on reviewing the underlying business data.

Where AI Helps

AI-based approaches to field mapping work differently from static rules tables. Rather than requiring an exact match between a source field name and a predefined list, semantic matching techniques assess what a field is likely to represent based on its name, its position, its data type and the values it contains.

This means an AI-based mapping tool can propose that "Cover Start Date" and "Policy Inception" both correspond to the same schema field, even though neither has been seen in exactly that wording before.

A further capability is learning from corrections. When a data analyst confirms or corrects a suggested mapping, that decision can be retained and used to improve future suggestions, both for that coverholder and for similar fields elsewhere. Over time, the proportion of fields the system can map automatically, with reasonable confidence, tends to increase.

This changes what is operationally possible in two respects. New coverholders can be onboarded faster, because the system proposes a starting set of mappings rather than requiring one to be built from scratch. And ongoing mapping effort shifts away from repetitive interpretation, towards reviewing the smaller number of fields the system flags as uncertain.

AI augments this work rather than replacing it. The judgement about whether a proposed mapping is correct, particularly for ambiguous or unusual fields, still rests with someone who understands delegated authority data.

Operational Considerations

Using AI-assisted mapping responsibly requires a few things to be in place.

First, a clear review workflow for low-confidence suggestions. Mappings the system is confident about might be applied automatically, while anything below a defined confidence threshold should be routed to a human reviewer before it affects downstream data.

Second, an audit trail. Every mapping decision, whether suggested by AI or confirmed by a person, should be recorded, including who approved it and when. This supports governance and gives oversight teams a clear record if a mapping is later queried.

Third, an awareness that mapping accuracy depends on the quality and volume of historical data available. A coverholder submitting a genuinely new format, with no prior mapping history, will naturally generate more suggestions requiring review than one whose format has been seen many times before.

Finally, mapping decisions with financial or regulatory consequences, such as those affecting premium or claims figures, warrant a higher level of scrutiny regardless of how confident the AI's suggestion appears.

Example

A London market managing agent receives monthly bordereaux from twelve coverholders covering agricultural risk across several territories. Each coverholder uses a different spreadsheet layout and terminology to describe the same underlying data, such as "policy inception" versus "cover start date".

The agent's operations team uses an AI-assisted mapping tool to automatically propose how each coverholder's fields correspond to the agent's standard schema. The tool maps the majority of fields automatically, based on prior confirmed mappings and semantic similarity, and flags a handful of unfamiliar fields for the analyst to review.

The analyst confirms or corrects these flagged fields, and the tool retains the correction for future submissions from that coverholder, reducing manual mapping time in subsequent reporting cycles.

FAQs

• What is a "standard schema" in the context of bordereaux?

  A standard schema is a consistent, predefined set of field names and formats that all incoming bordereaux data is ultimately mapped to. It might be an internal target template used by an insurer or managing agent, or a market-wide structure such as Lloyd's Core Data Record. Using a standard schema allows data from many different coverholders to be processed, validated and reported on consistently.

• How accurate is AI-based field mapping compared to manual mapping?

  Accuracy depends on the quality and volume of prior mapping data available. AI-based approaches typically perform very well on common or previously seen field variations, and improve as more mappings are confirmed over time. For unusual or ambiguous fields, a well-designed system flags the mapping for human review rather than guessing silently, which keeps accuracy under human control.

• Does AI remove the need for a data mapping specialist?

  No. AI reduces the repetitive effort involved in mapping common or previously seen fields, but it does not remove the need for delegated authority data expertise. Specialists remain essential for reviewing ambiguous mappings, handling exceptions and maintaining oversight of the overall mapping process.