A UK-based contract manufacturer with £8 million of annual revenue, sourcing components from suppliers in Taiwan and South Korea and selling finished goods to European distributors, will typically manage its currency exposure with a combination of a natural hedge — matching GBP costs against GBP revenues where possible — and periodic forward contracts arranged by phone through its clearing bank. The bank's treasury desk will quote a forward rate, the finance director will accept or reject it, and the contract will be logged in a spreadsheet alongside the outstanding payables it is hedging. This process works in the sense that it can be executed, but it produces outcomes that are materially worse than institutional hedging practice: the bank takes a wide spread on the forward, the contract is sized based on a forecast that is rarely updated, and the hedge ratio is set by intuition rather than by analysis of the company's actual FX exposure profile.
The access gap in FX hedging is structural. The sophisticated hedging tools available to large corporates — treasury management systems with integrated FX optimisation, algorithmic execution through direct market access, and risk analytics that model the company's currency exposure across all cash flows — require the scale to justify the implementation costs and the treasury function to operate them. Below a turnover threshold of roughly £50 million, the infrastructure is simply not available, and the corporate treasurer (if there is one) is using tools that are not fit for purpose. The FX desk at their clearing bank has no commercial incentive to change this: the wide spread on manual forward contracts is precisely the margin that makes the corporate banking relationship profitable.
The technology change that makes automated hedging tractable for mid-market businesses is the convergence of Open Banking-based cash flow visibility and algorithmic FX execution through direct API access to interbank rate feeds. A platform that can see a company's outstanding receivables and payables in real time — through Open Banking connections to its bank accounts and ERP data integration — can model the company's FX exposure dynamically and execute forward contracts sized against that modelled exposure rather than against a static forecast. Bound, which we backed in 2024, is building this automated hedging infrastructure specifically for the mid-market segment, using machine learning to model FX exposure from transaction data and executing hedges programmatically rather than by a finance director making phone calls to a bank desk.
We are not arguing that automated hedging eliminates FX risk or that every mid-market business needs a sophisticated hedging programme. The natural hedge — where possible, matching currency of revenue to currency of cost — remains the lowest-cost risk management approach, and businesses with primarily domestic cash flows are better served by keeping currency management simple. The specific opportunity is in businesses that have genuine multi-currency exposure — importers, exporters, businesses with foreign subsidiaries, or businesses operating in the global software market with revenue in multiple currencies — where the cost of hedging with a traditional bank is high and the cost of not hedging is potentially existential in a year with significant currency moves. For these businesses, the comparison is not "sophisticated hedging platform versus expert treasury team" but "automated hedging versus the finance director phoning the bank every quarter," and on that comparison the automated platform wins decisively on both cost and risk management quality.