LSEG is a global capital markets infrastructure provider operating multiple markets across asset classes, clearing and settlement services, analytics and index calculation and distribution. As an organization, we continue to increase our worldwide footprint, especially across North America, and our unique ‘open access’ model offers market choice and partnership across all of our businesses. By way of example, LSEG trading platform technology powers over forty other exchanges and trading institutions around the globe.
As you might imagine, the amount of data that our various LSEG businesses create is enormous, global in scope, and often extends unique or proprietary data sets. These are the areas where applying data science will not only help us make better decisions based on data and improve our operational efficiencies but also allow us to maintain competitive differentiation.
"“The key challenges for businesses adopting AI are developing internal capabilities and data availability”, and partnering with nimble external companies."
We believe that technology should be developed in a considered and rigorous manner, in partnership with clients to provide the right service and benefit to them. In fact, among other initiatives, LSEG is contributing to a market-wide steering group on Blockchain comprised of clients and other partners to look at the key challenges and opportunities. We have significant technical expertise to bring to the distributed ledger discussion and see the real opportunity in pre- and post-trade, particularly in risk management. Driving innovation and developing new products will significantly reduce risk and margin requirements while delivering the opportunity for deeper regulatory oversight.
Artificial Intelligence: Our Approach
LSEG takes a business-driven approach to utilizing data analytics. In this way, in-house data scientists with operational and practical knowledge of our business work closely with company analysts to solve business problems. Marrying deep business knowledge with skilled data scientists is a key differentiator for us. One of the emerging applications across the firm and for the capital markets industry is the use of artificial intelligence (AI).
One of the earliest AI applications for LSEG was in the equity market trading surveillance space. Some time ago, we extracted a data set from a market’s order book, normalized the data, stored it in a manner that leveraged the then-emerging big data tools, and began running machine learning type algorithms jointly with an external partner. Our goal was to gain experience with big data tools, like Hadoop and Spark, and to learn whether this type of technology could add value to our already robust market surveillance program. We found that AI did significantly add value so we have implemented these tools to future enhancements of our surveillance systems.
We intend to expand our surveillance capabilities to include formerly separate data sets where patterns of behaviour and other business intelligence have not been explored. For example, the ability of our surveillance team to integrate unstructured/semi-structured data with market data will be a powerful tool to detect or deter not compliant behaviour.
More broadly, LSEG is exploring ways to centralize and control its data so that analytics may be securely applied. We expect these efforts to provide not only business intelligence to LSEG’s customers but also efficiencies around regulation, risk and operations.
In addition to market surveillance, which is a critical element of LSEG’s business, applying AI to operations might yield important improvements to operational efficiency. For example, AI might provide advanced detection of potential hardware failures before they occur. Additionally, we are considering customer trading patterns to determine whether AI could yield better customer experience by balancing customer hardware use in a more optimal fashion.
Leveraging LSEG’s unique data sets, AI will become a competitive differentiator for us. We will be able to offer our customers additional insight into not only their data but also data that is anonymously provided by the wider community of users–this has clear potential benefits for our customers.
Over the next five years, LSEG will continue to invest in AI because our data is a critical strategic asset for our customers;using AI will provide us with additional tools to add value to this data, responding to the demands of our customers.
We think that most companies are focusing significant efforts on collecting data and beginning the analysis process with AI or other machine learning techniques. The industry is at the stage where AI is rapidly developing so it’s fair to assume that adoption rates will be very high in the coming years. This rapid development means that technologies that just a few years ago were considered emerging technologies are now widely available as open source products.
The key challenges for businesses adopting AI are internal capabilities and data availability, and partnering with external companies. Senior management need to commit resources to an AI-driven business environment that aligns with the strategic direction of the firm. This means that business leaders must understand what data science is, including AI, what its limitations are in the context of their business and invest accordingly to identify and solve valuable data science problems.
It is not enough just to build the technical environment to allow AI to operate nor is it enough just to have data because the data has to have inherent value. Ultimately, if you don’t have a team that understands how to identify and solve data science problems that align with the overall business strategy then it will not add value for the business.