Enterprise AI applied to specific treasury operational workflows.
The clearest way to understand what responsible enterprise AI looks like in treasury is to see it applied to specific problems. What follows are worked examples — drawn from experience and from practical prototype work — of how enterprise AI can support treasury operations while preserving the judgment, controls, and trust the profession demands.
Each case study reflects the same principles: multi-horizon reasoning, explainable outputs, and the treasury professional firmly in the loop.
The objective is not to demonstrate AI in isolation, but to show how domain expertise, enterprise architecture, and AI combine to redesign an end-to-end Treasury process.
Case Study 01
Enterprise AI for identifying, quantifying, and supporting decisions on FX exposure and hedge effectiveness.
FX exposure and hedge management sit close to the strategic heart of corporate treasury. Every decision has consequences on the balance sheet, on cost of hedging, and on the organization's tolerance for risk. Enterprise AI in this domain must therefore be exceptionally disciplined about explainability, accountability, and where the human professional makes the call.
This case study follows a multinational group through its monthly cycle: consolidating FX exposures across subsidiaries, netting intercompany positions multilaterally through an in-house bank, and arriving at the group's true residual exposure — the only position that could ever need an external hedge. From there the system recommends the hedges, sized and justified, for the treasury professional to approve. The professional remains firmly in charge of every consequential decision.
Proof of concept
A proof of concept takes this further than the page can. It runs a full month-end cycle for a multinational group — retrieving FX exposures, classifying what is in scope, netting multilaterally through an in-house bank, and arriving at hedge recommendations for the treasurer to approve or override. The emphasis throughout is on governance: every step is recorded, and the human makes every consequential call.
A multi-agent architecture consolidates what is normally a fragmented, multi-team process into a single auditable workflow. The separation between agents mirrors segregation of duties — a familiar control principle rather than a technical convenience.
Each recommendation — and each deliberate decision not to hedge — carries its own reasoning, so the professional reviewing it can see why, not just what.
Every figure traces back to its source through a verifiable chain, so review and audit are a matter of inspection rather than reconstruction.
The pipeline works from a normalised extraction, so the same approach applies regardless of the underlying system of record.
The proof of concept is best seen live — the multi-agent structure working through the full cycle, reducing what is normally days or weeks of work across several teams to a matter of seconds and minutes. If you would like to see it, or to talk it through in the context of your own treasury, please do get in touch.
From use case to architecture
The value is not in any individual AI agent. It is in the architecture that connects Treasury policy, transactional data, deterministic controls, AI reasoning, human judgment, and execution into a coherent and auditable process.
For the purpose of this proof of concept, the synthetic data is modelled in SAP format — reflecting the In-House Banking, AP, AR, and Treasury modules. That said, the pipeline works from a normalised extraction, so the same approach applies regardless of the underlying system of record. It is a demonstration of how a multi-agent AI architecture operates within a complex treasury workflow.
Case Study 02
Enterprise AI for the daily, tactical, and strategic horizons of treasury cash forecasting.
Cash forecasting is among the most demanding processes in corporate treasury, and the difficulty is structural rather than a matter of effort or skill. Seven challenges recur across almost every organisation.
Data from different sources. The system of record, the banks, the treasury system, submissions from subsidiaries — each with its own format, timing and definitions. Assembling a single reliable view is often the longest part of the work.
Timing. A forecast can have the amount exactly right and still be wrong, because the cash moved on a different day. Due dates are not payment dates.
Variables that change with the horizon. What determines today's closing balance is not what determines the position thirteen weeks out. Near-dated cash is largely a matter of what is already known; longer horizons are a matter of behaviour, seasonality and genuine uncertainty.
Scenario analysis. Asking what happens if a large receipt slips a week usually means exporting to a spreadsheet, at which point the analysis leaves the governed system behind.
Explainability and accuracy measurement. It is easier to produce a forecast than to say why it moved, and easier to record that it missed than to establish why.
Two kinds of uncertainty, treated as one. Some uncertainty is a gap in knowledge — an amount not yet available, a payment date not yet confirmed — and can be closed by finding out. Other uncertainty is inherent: a customer who has always settled somewhere between day 28 and day 35 will go on doing so, and no quantity of historical data will fix the date. Conflating the two is costly in both directions. Treating a knowledge gap as irreducible means accepting an error that could have been resolved; treating genuine variability as a knowledge gap means chasing a precision that does not exist, and presenting a forecast as more certain than it is.
The ceiling of existing tools. Machine learning finds patterns in historical data and does it well. But it predicts without reasoning about why it was wrong, treats a contractual payment and an estimate as the same kind of input, and answers whether or not it has grounds to. Anything unstructured — the remittance text where the identifying detail sits — needs rules that must be written, then rewritten each time a counterparty or a bank changes a format.
These challenges share a shape: each requires judgment about information, not simply the processing of it. That is why they have proved durable.
Agentic AI is the first treasury technology in a generation that does something genuinely new — it reasons across data rather than executing rules against it.
Every advance before it made specification faster or pattern-finding sharper. This is a different capability, and it is why these particular problems now look tractable. The question itself has not changed: what will the cash balance be, at the end of or during a given horizon, and with what confidence? What has changed is what can be brought to bear on it.
Sources that do not agree can be reconciled at speed, including bank data that is often available but unused because it creates no accounting entry. Unstructured text can be read for meaning rather than parsed by rules, which removes both the need to anticipate every variant and the maintenance that follows from having tried. Method can follow the horizon, rather than forecasting statistically what is already known contractually. A system can distinguish what it could find out from what is genuinely unknowable, and act differently on each, rather than treating every uncertainty as the same problem. And a forecast can state its confidence rather than imply a precision it does not have — which is what allows a professional to act decisively on an uncertain number.
Where the system lacks information, it says so and asks. Judgment about the things data cannot see stays where it belongs.
For the purpose of this proof of concept, the synthetic data is modelled in SAP format — reflecting the cash-flow structure SAP uses for forecasting — together with simulated bank statements in ISO 20022 format for settled cash. That said, the pipeline works from a normalised extraction, so the same approach applies regardless of the underlying system of record.
If one of these case studies mirrors a challenge you're working on, or you'd like a second opinion on an approach you're considering, I'd be glad to hear from you.