By Kathy Maher, EVP Operations, 9th Gear
The foreign exchange (FX) industry has experienced a fascinating evolution since its humble beginnings. Long ago, in the 70s, the Bretton Woods system broke down, and a new world of floating exchange rates fueled explosive growth to $80 billion in settlements per day by 1980. That uptick in the market, however, was only the beginning. Settlements surpassed $1 trillion per day in the early 1990s and today, currently top over $5 trillion daily.
As a result, it is worth a review of the current structure of the marketplace, particularly how the middle and back offices operate, to anticipate the ongoing needs of FX trading and how it might continue to sustain this enormous market size.
In the late 1970s, FX trading was supported by two main technological innovations to “automate” the back office: EFT (electronic funds transfer) and the birth of the internet.
In 1978 there were over 123,000 telex connections, many of them in FX trading rooms and “wire rooms,” moving funds around the world. In these early days, parties would exchange “test keys” and then wire funds. During the same time, the Information Age was taking hold with the introduction of EFT and the inception of the Society For Worldwide Interbank Financial Telecommunication (SWIFT). Along with these game changers came the first commercial application of the internet in 1980. These events laid the ground-work for the new mantra for the back office: STP (straight through processing).
FX operations were ripe for serious automation and, with standardization of SWIFT messages, vendor-supplied FX applications and internally-developed offerings were implemented around the world. Improvements in confirmation matching, payment processing and nostro reconciliation supported this exploding volume growth. Operational efficiency and effectiveness were clearly on the rise.
With this growth came higher risk and thus increased management oversight, as FX revenues were no longer a footnote for many global banks. The concept of delivery risk or Herstatt risk had increased focus due to massive money flows resulting from FX trade volume growth. Banks poured millions into credit and market risk systems to measure and monitor risk, often at the expense of funding additional STP initiatives in the back office.
Out of these risk concerns came the concept of netting to reduce the magnitude of funds flowing through the payment systems, and in turn, the delivery risk between counterparties. Bilateral netting agreements and supporting technology improvements led to reduced risks and in many cases reduced costs. Executing netting agreements one by one is time consuming and expensive so the industry came together and launched a netting utility in 2002 known as CLS (continuous linked settlement) that has been successful in pushing delivery risk levels down significantly.
Faster processing cycles, lower transaction costs, higher STP rates, better risk measurement and lower levels of delivery risk have all been realized steadily since the early days of the 1970s even while absorbing double-digit CAGR over the last four decades. But diminishing returns have set in as efforts to reach 100% STP rates and continued efforts to lower processing costs have tapered off. A 2018 EY study showed that increasing operational efficiency remains a priority at most financial institutions.
A desperate need for change
Continuous improvement efforts are admirable, but not transformative. Transformation and the successful outcomes that can result require that the process be re-imagined. Currently, FX spot trades settle two business days from the date the trade is negotiated. Besides historical tradition, why is the delay required? It certainly adds risk.
First, traders making markets in currencies normally work within position and credit counterparty limits without regard to whether they have the currency on hand to deliver. They worry about that later. Thus, even with state of the art technology, one cannot deliver what one does not have. How can this be solved?
The simple answer is to make liquidity readily available. With the issue of funding resolved, let’s look at the operational process to see where inefficiencies exist and question how they can be eliminated.
Once a trade is executed, the trade passes to the middle offices to confirm trade and payment details with the counterparties. SWIFT has designed messages to automate this process and STP rates have risen to the 90% range, but that 100% goal remains elusive. Looking at the reasons for match exceptions will shed some light on the underlying reasons for these mismatches.
Not all counterparties use SWIFT, especially non-financial institution customers, and trade confirmation becomes more manual and labor intensive. However, even those with state of the art matching engines are not at 100% rates.
In the current paradigm, reaching 100% on a consistent basis has a major barrier. As long as a trade is booked in the separate books and records of each counterparty, the potential for discrepancies presents itself. Although technology tools are available, the concept of a mutual ledger, shared by all, has not become an industry standard.
A promising solution
Looking to the future, two innovations have the potential to transform the FX business with the result of clearing and settling trades in minutes, not days. Technology advances in digital ledgers and blockchain make it possible for counterparties to access the same book of record using immutable ledgers supported with smart contracts, producing a single source of the truth.
While this single innovation, increases effectiveness, it must be coupled with advances in efficiency to reach the goal of near immediate settlement. The answer also requires solving the liquidity issue by providing access to funding as needed. Coupling an FX trading marketplace with a lending platform to provide funding for trades on-demand answers this call.
The future of FX brings with it the potential for long-overdue upgrades to its processes. We can expect the trading procedure to experience a meaningful shift from a cumbersome, time-intensive one to a simpler, streamlined task – successfully re-imagining the system to the benefit of the entire banking industry.
2020: THE YEAR BLOCKCHAIN COMES OF AGE
– By Rob Coole, VP of Cloud Technologies at IPC
Despite headlines over the years stating that blockchain will change the world, it has not been validated or deployed at such speed and scale like other new technologies such as AI or cloud. Blockchain’s intensive power consumption, reliance on multiple servers and the sheer expense of it, are some of the main reasons cited. In the past, the hype had not met the reality.
But in 2019 blockchain came into its own. With more understanding of what blockchain can do for financial markets and its use points becoming more clear, real-life deployments and advances have started to develop. 2019, for instance, was the year when we saw new blockchain alliances such as Enterprise Ethereum Alliance, increase of blockchain start-ups and the introduction of new infrastructure projects.
Additionally, Gartner’s own Hype Cycle for Blockchain Technologies shows that blockchain is sliding into the “Trough of Disillusionment” – predicting that over time, “permissioned blockchains will integrate with public blockchains, and will take advantage of shared services while supporting the membership, governance and operating model requirements of permissioned blockchain.” Additionally, Gartner predicts that blockchain will be fully scalable by 2023. IPC’s sense of the future of blockchain, particularly in the enterprise space, is just as positive. We are seeing customers truly learning about the practical purposes to deploy, leading to more investment in time and money in blockchain.
Blockchain is suited for complex, collaborative, multi-party, and critical application use-cases and one reason why the hype around blockchain took much longer than some predicted. Adoption in highly regulated, complex markets such as the financial services industry shouldn’t be a surprise. However, we are now seeing a rise in organisations taking a competitive advantage by adopting next-generation blockchain, rearchitected and redesigned to meet the stringent requirements needed for the financial industry.
Next-generation blockchain organisations are leading the way showing the industry how the technology can be used intelligently for the world we live in today. R3, an enterprise software company for example, is working with an ecosystem of over 200 financial institutions, regulators, trade associations, professional services and technology companies to develop Corda, a Blockchain platform designed specifically for businesses to deliver two interoperable and fully compatible distributions of the platform that address issues such as transactional certainty, data privacy, and the scalability limitations.
Both application service providers and subscribers should exploit service providers with products and solutions so that they are not left behind. It is important that partners are complementary to both service providers and subscribers in terms of operational level integration to complement application services. It is critical for adoption success.
We are now seeing blockchain have real value with the integration and support from the hyper-scale platform community such as Microsoft Azure and AWS together with open industry platforms, such as IPC’s Connexus Hub, that creates end-to-end solutions that solve business problems.
We are, like many technology sectors, seeing a move to an API approach. APIs support partners integration and gives institutions the ability to easily access data, provide insights and inspire innovation for the market need.
Service providers, like IPC, can play a critical role here by supporting operationalisation in the systems-oriented context. Such providers are a natural connector embedding connectivity to key market participants. IPC, for example, enables access to all asset classes with over 2,000 sell-side firms, 4,000 buy-side firms and over 75 exchanges in its vast, diverse ecosystem.
Of course, 2020 has and continues to bring new challenges, with the COVID-19 pandemic affecting every aspect of our lives. The World Economic Forum, however, believes technologies such as blockchain “will benefit all countries currently impacted by COVID-19”, as it provides an efficient approach to reduce trade cost on a global scale.
Digital initiatives such as blockchain is non-partisan and open to all which allows users to act quickly at low cost with low barriers for innovation – all valuable factors in getting the global economy back on its feet. So, although blockchain adoption was slow in its early stage, 2020 seems to be the year blockchain comes of age.
AI IN THE FINANCE SECTOR: WHAT’S NEXT?
By Rui Vasconcelos, Product Manager for AI/ML at Canonical – the publisher of Ubuntu
The last few years have seen the promise of general AI acclaimed across multiple industries and this vision has been particularly strong in the finance sector. We’ve currently hit the trough of the hype curve and it will take some time for engineering solutions to deliver on the touted promise. The potential is so great, that hope for general AI will require a longer term and collaborative investment, rather than a quick ROI for a single financial company. As a result, we need to see a continued collective effort from organisations in the direction of making general AI a reality – whether that is in the near future or further in time.
Artificial intelligence within financial organisations has developed from an almost unfathomable vision into tangible deployments, with applications ranging from back-end decision making to front-end customer-facing services. Financial services companies are now placing a much greater focus on AI/ML and are rearchitecting their IT and business operations to take advantage of what this new technology can offer. However, these implementations are what is known as ‘narrow’ AI, which is focused on a single or limited task and operates within a pre-programmed state. Almost all of the AI that surrounds us today is narrow AI. Everyday examples within the financial industry range from Robo-advisors to tailored credit and insurance tools. In distinction, general AI is a progression of this and is often described as an AI solution that can solve a wide range of financial services issues – from natural language understanding to anticipating risk and detecting fraud – with the additional advantage of self-learning to solve any problem without human intervention.
Narrow AI is goal-oriented and solves a particular problem, which is not necessarily bad. We have seen AlphaGo perform a singular task (playing the complex game of Go) and beat the top human expert at it. Organisations focused on being highly competitive in specific use-cases, should concentrate on narrow AI, however it is a short-term win. Those looking at wider-range problems and planning to gain long-term competitive edge need to consider investing in work that will make general AI more accessible, benefitting both the company and society in the long run. Getting there will harness tools and insights that will be very valuable to other financial services applications, even if we do not reach general AI in our lifetime. Where a ‘narrow’ AI would take into consideration historical stock prices to make time-series predictions, general AI would look into all types of accessible data that might influence the mood of investors on that day.
It’s unsurprising that AI development is a resource heavy and challenging process, and general AI development will be even more so. However, we possess an unparalleled capacity today to move it forward, both in terms of computation and human collaboration. The open source community may be able to help tackle some of the hurdles to general AI development by encouraging collaboration as well as pooling knowledge and resources. For instance, open source software allows IT teams in finance companies to benefit from frameworks, data sets, workflows, and software models in the public domain at reduced costs. In addition, the open source community sees projects as a shared responsibility, so provides an extra layer of security by continually monitoring source code for potential flaws and vulnerabilities.
A further advantage of the open source community is that it assists financial businesses to overcome the AI skills gap – one of the most frequently discussed obstacles to AI adoption. In fact, recent research shows that a third of IT teams cite a lack of skilled people and difficulty hiring for required roles as the third most-common challenge. The first hurdle is a lack of institutional support from within the business. In another study, technology’s lack of transparency was also cited as a major hurdle. With collaboration promoted at its very core, an open source approach to AI allows smaller IT teams to benefit from the wider expertise of the much broader community.
Open source will be fundamental to democratising the development of general AI. Financial services organisations who are invested in refining and improving AI for the benefit of their own operations and society will look to open source for future development. However, realising general AI will require long-term investment. Without it, the likelihood of reaching general AI in our lifetime is low. So, it’s up to financial services businesses to start concentrating resources into general AI now to make this future a reality in a short timeframe.
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