13 Feb 2024


Transformation of financial markets

The coming wave of technological change through artificial intelligence, big data and blockchain technology is set to fundamentally change derivatives markets. Eurex spoke with Helen Hartwell, Head of Exchange Traded Derivatives Client Consulting at UBS Investment Bank, about how digitalization is driving an evolution in the marketplace, front-to-back.

Where is disruption coming from in listed derivatives markets?

The transformation of financial markets through digitalization is a trend we’ve seen for some years now, encompassing areas from customer experience to operational efficiency to risk management. In listed derivatives, there has been significant investment in digitalizing execution, through algorithmic trading and further trading desk automation. What we see more recently though is broader adoption of digital technologies in clearing, particularly cloud computing, enhanced data analytics and AI/machine learning. The discussion around optimizing collateral processing through distributed ledger technology has also become more prevalent.

With technological advances, we see clients’ expectations changing and many are undergoing their own digital transformations. There is a growing need for personalized solutions that are tailored to a client’s ecosystem, as well as increased focus on intraday data provision. We also see cybersecurity, data privacy and digital operational resilience becoming client priorities, in the context of a marketplace that is increasingly interconnected and reliant on third party technology providers. All these factors are impacting how we think about our offering and platform.

Where are we now in the industry with regards to tokenization and artificial intelligence?

The listed derivatives market is still at a fairly nascent stage on tokenization, but we are seeing more and more tangible examples live in the broader industry, outside of listed derivatives.  A primary application already in use today is reducing operational friction and risk inherent in settlement processes. For example, Fnality Payment System, which at UBS Investment Bank is led by IB Strategic Ventures, is a digital decentralized financial market infrastructure with an integral ‘digital cash’ asset. Fnality creates efficiencies across wholesale payment processes by enabling instant, peer-to-peer, multilateral settlement with digital, programmable cash. Importantly, Fnality is recognised and regulated as a 'Payment System' in the UK, by the UK's Payment Systems Regulator. It is the world’s first regulated, institutional-grade payments system based in Distributed Ledger Technology, and in December 2023 completed the first institutional transactions using digital cash, backed 1:1 by cash held at central bank.

When it comes to data analytics and AI, we are seeing a shift from providing data as a service towards providing insights on that data. As a listed derivatives provider, our clients are looking to us to provide added value, helping them spot trends and optimization opportunities, and we are building solutions in this space. AI supercharges that as it is very effective at spotting patterns and anomalies within the data that are not visible to the human eye.

What are the barriers to change and the challenges to adoption?

There are many Proof of Concept initiatives in AI today, but a big challenge for firms is the ability to safely and securely scale these capabilities. This has to start with a solid data foundation. Not only do you need a lot of data, which firms in our industry have more than ever before, but you also need high quality data. A common challenge is that the critical data points are held in disparate data sets housed across different systems and at varying degrees of quality. To move beyond basic data analytics, the key is how well a firm can bring together and connect those data silos, making sense of the data holistically and making it accessible for business decision-making. We see cloud computing as a strategic enabler to address analytics at scale, and at UBS we’re transforming our data architecture to achieve that.

It is not just about the technology though – implementing these capabilities in a business sense means a change to the whole operating model, from front to back. Take the workforce, for example. There is a growing realization that to deliver these new capabilities effectively, a hybrid team is required. Of course, you need data scientists and engineers, but there is a broader requirement for people who have the business domain expertise and can bridge a path between the needs of the client, the offering of the business and potential of the underlying technology.

What are the barriers to growth across the industry?

There is a high degree of interconnectedness between participants in the listed derivatives marketplace.  Many of the digital technologies we’ve talked about rely on the power of the network to realise their full potential, but you have firms at very different stages of maturity when it comes to adoption. Digitalization across listed derivatives clearing will therefore be a gradual evolution taking into account the complex interaction between firms.

New approaches with these technologies, particularly AI, also raise new questions which we need to tackle in the context of the highly regulated environment in which we operate. Clients need to know that their data is protected, that confidentiality is maintained, and they need transparency. When it comes to broadening use of generative AI, these considerations will only grow.

Where do you see the key applications for AI in listed derivatives?

AI will undoubtedly revolutionise the listed derivatives marketplace from many angles, very few areas will be untouched. From a post-trade perspective, I see huge potential to apply digital technologies across operations. The industry has been working for many years to improve efficiency and reduce risk through automation, and at UBS we have achieved a really high level of straight-through-processing.  To provide the very best service to clients it is key to identify opportunities to further drive efficiency in their operational flows. With machine learning, we can look at vast amounts of historical data to identify patterns within processes at scale, automatically diagnose the causes of exceptions and recommend optimization opportunities. We can even look to predict where there might be problems in future workflows, for example in relation to trading patterns. This kind of approach is already being applied in a reconciliations context, where machine learning algorithms can be used to enhance matching capabilities, detect anomalies and drive continuous improvement in future processing.

There are more applications in client service, in areas such as efficiency in email workflows, using natural language processing to route emails to the right teams or for sentiment monitoring to identify where an email needs closer attention. We are also seeing more and more applications in the product space, such as identifying trends in product activity which we can use to alert clients to opportunities.

How about blockchain? Where is the industry today with regard to that technology?

The advancement of distributed ledger technology in the listed derivatives marketplace has been at a slower pace than many anticipated, but it holds a lot of promise. The hot topic today is tokenized collateral settlement where it could offer margin efficiency and reduced risk through instant settlement. In terms of where we are right now, while there have been several examples outside listed derivatives, we are yet to see it take off. That is due in part to the complex and challenging nature of any implementation in a very interconnected industry, but also down to a degree of uncertainty in the regulatory framework. While the industry is in an exploratory phase right now, in my view the next phase will likely be a hybrid model rather than a leap to a fully tokenized state. Interoperability and integration with the traditional model will be key for further progress.

What are the long-term implications for market structure?

It is clear that digital technologies are advancing rapidly, and AI is set to play a significant role in shaping the future of our marketplace. In post-trade, I think we will see more and more focus from firms on how to scale their capabilities on this in a way that enables the delivery of solutions that offer true and lasting value for clients. A transition needs to happen with the way that firms approach data and the pace of AI adoption in our industry will go hand-in-hand with this. Throughout, digital operational resilience planning needs to remain at the core of the discussion.

We will also see a broadening of the kinds of use cases that we use AI for. While current use cases are fairly targeted, as the technology advances, particularly in the generative AI space, we are going to see a broadening and increased ability to handle ambiguity and judgement. This raises many new questions, so there is plenty of road to travel on this, and something that I look forward to partnering with clients and other market participants on in the years to come.

Join the Derivatives Forum Frankfurt on Feb 28-29 and hear more about this topic.


  • Helen Hartwell, Head of ETD Client Consulting, UBS
  • Daniël Rood, Head of AI Startups EMEA, Web3 Google Cloud
  • Rudolf Siebel, Managing Director, BVI
  • Christoph Hock, Head of Multi-Asset Trading, Union Investment

Moderator: Annette Weisbach, Correspondent Germany, CNBC