Juliette Kennel Head of Securities and FX Markets at SWIFT
Juliette Kennel Head of Securities and FX Markets at SWIFT

The newly enriched FX data service from SWIFT

The newly enriched FX data service from SWIFT uses real transactions to measure activity share and competitive position on an instrument-by-instrument basis. We spoke with Juliette Kennel, Head of Securities and FX Markets at SWIFT to learn more about it.

“We have a significant FX data set. We know that a very large percentage of all messages confirming FX trades across the world pass through SWIFT,” says Juliette Kennel . As a co-operative, historically SWIFT has not released figures for value transported over the network, but FX trading amounts to some £5 trillion per day, and a significant part of that is confirmed over SWIFT. SWIFT also provides the network channel to CLS, which accounts for another significant amount – the total can be calculated at around 50 per cent of all transactions confirmed. “We’re a major infrastructure for the FX industry, although few participants realise that,” says Kennel.

Change on the way

This year, we can expect that to change. SWIFT handles a vast amount of potentially valuable data, and that delivers an opportunity to add value by using real transactions to enable users – principally banks – to measure activity share and competitive position on an instrument-by-instrument basis. This has never happened before, not on the scale that could be enabled by SWIFT, but advances in data analytics have focused attention both on the amount of data that SWIFT holds, and also on the learning that could be drawn from it. “We want to evolve our product offering,” says Kennel. “We want to protect what we’ve got, but we want to do more as well. Given our market position, we think the best way to do that is to add value through data services.” Unlocking the value of around half of the confirmation data – the very big confirmation data – generated by the FX market on a daily basis will bring significant benefit to the industry, and to SWIFT as well. The co-operative, which traces its history back to an initial meeting of 15 banks in 1973, is widely associated with cross-border payments, but FX has been part of the mix since its inception. But how to release the value of all that data into the FX market?

Significantly for data-analysis purposes, direct FX-trade confirmation via SWIFT is standardised through use of the MT 300 messaging standard; such standardisation is set out as a principle in the current iteration of the FX Global Code of Conduct, published by the Global Foreign Exchange Committee in August 2018. Both parties to a bilateral trade will either settle in CLS or generate a confirmation and then a payment message netting, , is an exception to this, although there is of course a connection to be drawn between the netted FX trades and the resulting downstream payment. “This means of course that a large number of MT 202 messages [for funds transfer between financial institutions] sent over SWIFT have an origin in FX trading,” says Kennel.

SWIFT gpi in numbers
SWIFT gpi in numbers

SWIFT’s gpi 

SWIFT’s gpi – global payments innovation – now enables correspondent banks and their customers to make fast and secure payments globally, with full transparency over where a cross-border payment is at any given moment. The SWIFT gpi Tracker gives end-to-end visibility on payments. Discussing the current wave of fintech and other initiatives in the payments space, Kennel says: “The SWIFT gpi is a great way of showing that correspondent banking is evolving and is fit for purpose for the future.” SWIFT announced its “Tracker for all” functionality in October 2018 – a simple version of the SWIFT gpi Tracker for all SWIFT members – and will implement universal gpi adoption across the banking industry by end-2020. “Whenever there’s an FX trade, there’s a payment sent out and a payment received, and the SWIFT gpi Tracker enables far more efficient management of liquidity,” says Kennel.

There is more

For the FX industry – there’s more. Trade confirmations exchanged using the MT 300 messaging standard themselves generate significant data that until now, SWIFT has not been able to release. At the end of 2017, SWIFT gained its own board’s approval to mine additional data to  extract value dates and other information, such as whether the trade is an FX spot trade, or FX forward, or and FX swap. SWIFT has now processed the entire data set for 2018 – upwards of 200 million transactions – to identify every instrument type. “That’s a massive step forward,” says Kennel. “Not only can you see who confirmed to whom, what region, what currencies; we now know the instrument type as well.”

SWIFT have been working with five banks – in a “design partnership” – to identify all the potential value-adds of the enhanced data, and that work will form the basis for new SWIFT products and analytic tools in the FX space. The aim is to deliver a comprehensive range of new products based on a clear understanding of client needs. “The idea is, we’re working with clients to work out what’s best for them,” says Kennel. Not surprisingly, there is considerable interest already from banks – beyond the five in the design partnership – in SWIFT’s new FX data range – and talk already of further launches and events later this year. In the time-honoured phrase – watch this space.

The full picture

For the first time it is now possible for SWIFT to show the relative percentage breakdown of instrument type for the global FX market. The following tables compare the breakdown estimated by the BIS for the market in April 2016, to what is now available from SWIFT’s own metrics, and to those publicly published by CLS in their monthly market reports.

Source: SWIFT BI Watch

It is interesting to note that the combined SWIFT and CLS percentage share by instrument correlates closely to the shares published by BIS back in 2016. SWIFT estimates that this combined share equates to approximately two thirds of the daily traded value of the entire global FX marketplace. In addition, SWIFT can now show the underlying net number of trade transactions behind each instrument type as shown in the table below.

Source: SWIFT BI Watch