How Do We Measure Value Beyond Direct Cost Savings?

When a delegated authority team asks for investment in improving how bordereaux and other DA data are processed, the first question is almost always about cost.

How much headcount will this save? How many hours of manual work will it remove?

These are reasonable questions, but they only capture part of the picture.

Improving DA data flow also affects how quickly queries are resolved, how much bad data reaches underwriting and reserving, how exposed the organisation is to compliance and audit risk, and how experienced staff spend their time.

None of this shows up in a simple cost-savings calculation, yet it is often the difference between a business case that succeeds and one that stalls.

Why cost savings alone don't capture the full picture

Cost-savings framing is popular because it is simple to present and easy for finance stakeholders to evaluate.

If a process change removes two full-time roles, that number is concrete and easy to defend.

But delegated authority operations carry risk and relationship value that a pure cost calculation ignores.

A delay in processing a bordereau can mean a coverholder waits longer for confirmation, a reserving team works from incomplete figures, or an oversight team cannot demonstrate timely review to regulators or auditors.

None of these outcomes are captured by headcount reduction alone, yet each represents real operational exposure.

When a business case only asks "how much cheaper is this," it systematically understates the value of removing that exposure.

Traditional approaches to measuring operational value

DA teams have long tried to capture this broader value, typically through a mix of informal and manual methods.

Service level tracking against coverholders is common, recording whether bordereaux were received, processed and queried within agreed timeframes.

Some teams run periodic manual audit sampling, pulling a batch of records to check accuracy and flag recurring issues.

Others rely on anecdotal feedback: a broker relationship manager noting that a particular coverholder complains about slow query turnaround, or an underwriter mentioning that reserving figures were revised after late data corrections.

These approaches are useful but limited. Service level tracking is often inconsistent across coverholders. Manual sampling only checks a fraction of the data. Anecdotal feedback is real but difficult to quantify or defend in a formal business case.

The result is that non-cost value is frequently discussed informally within DA teams, but rarely presented with the same rigour as cost savings when a business case is put together.

Where AI helps quantify and realise value

AI-enabled processing of bordereaux and other DA data can generate consistent, granular operational data as a natural by-product of processing every submission the same way.

Rather than sampling a subset of records, every bordereau processed can be logged with turnaround time, exception rate, query volume and the nature of any data issues identified.

This creates a dataset that did not previously exist in most operations: a consistent, organisation-wide record of how quickly and accurately data moves through the process.

That data can then be used to evidence value categories that were previously anecdotal. A reduction in average query resolution time, a fall in the proportion of bordereaux requiring correction, or a shift in analyst time from manual data entry to exception review can all be measured directly.

AI does not decide which of these categories matter to the organisation. That remains a judgement for DA professionals, who understand which risks and relationships are most significant to their business. What changes is the availability of consistent operational evidence to support that judgement.

Building value measurement into ongoing operations

Measuring non-cost value works best as an ongoing operational discipline, not a one-off exercise built solely to support a single business case.

Start by selecting a small number of value categories that matter to your organisation and its stakeholders. Finance may be most interested in capacity reallocation, compliance teams in audit readiness, and operations in query turnaround.

For each category, identify a metric the organisation already collects or could reasonably start collecting: turnaround time, error rate, query volume, or hours spent on exception handling versus manual entry.

Build these metrics into regular reporting, alongside any cost figures, so that value is tracked consistently over time rather than reconstructed retrospectively when a new investment case is needed.

This approach means that the next business case does not start from a blank page. It draws on evidence that has already been building for months.

Example

A managing agent's DA oversight team is preparing a paper to secure investment in improved bordereaux processing.

The finance director asks for the cost savings case, but the operations lead wants to show the full picture: reduced query turnaround with coverholders, fewer data errors reaching the underwriting and reserving teams, and freed-up analyst time now spent on exception review rather than manual data entry.

The paper presents cost savings alongside three additional value categories, each backed by operational metrics already collected: average query resolution time, bordereaux error rate, and analyst hours reallocated to exception handling.

The broader case secures investment that a cost-only paper had previously failed to justify.

FAQs

• What are examples of value beyond cost savings in DA operations?

  Faster turnaround with coverholders, improved data accuracy reaching underwriting and reserving, reduced compliance and audit risk, better broker and coverholder relationships, and freed-up staff capacity for judgement-based work are all common examples.

• How do we measure value that isn't purely financial?

  Use operational metrics the organisation already collects or can reasonably start collecting, such as processing turnaround time, error and query rates, and the amount of staff time reallocated away from manual data entry. These act as measurable proxies for value that isn't directly financial.

• Does AI help measure this value, or just create it?

  Both. AI-enabled processing can create value through faster, more accurate handling of bordereaux, and it also generates the consistent operational data needed to measure and evidence that value. Deciding which value categories matter most remains a business decision for DA professionals.