Data is Achilles Heel of AI07.27.2018
For Capital Market Firms, Data is the Achilles Heel of Artificial Intelligence
By Michael Alexander and Vijay Mayadas, Broadridge
How Artificial Intelligence will revolutionize financial services is a hot topic among Wall Street technologists. But, just as a home renovation might be delayed by the discovery of extensive foundation problems that need to be repaired first, the financial services industry must improve the quality of its data before it can realize the benefits of AI.
Early front-office AI applications, such as Deutsche Bank using AI to predict equity pricing, have won headlines. But there is also significant momentum around applying AI to post-trade processing, compliance and risk management: Instead of manually searching for a needle in a haystack to learn why a trade failed to identify a real issue, AI can pre-emptively flag problems and solve the exception. However, while many capital markets firms have explored AI initiatives, few of these pilot programs (less than 15% according to Forrester Research) have made it into production to yield real business value. As a new study by McKinsey Global Institute notes, “a lot of companies are still keeping (AI) at arm’s length, fearful that it’s too complex and won’t pay off fast enough.”
But AI has the potential to leap beyond incremental advances to reimagine processes. The first step to realize these changes is firms improving and consolidating their data in-house thus improving risk and compliance management by reimaging processes internally. Then, if a firm joins with other capital market firms that are on a common platform, all those companies enjoy the network benefitof being able to overhaul processes across the entire industry. Instead of a firm making decisions based only on its own data, it would have a much larger pool of information to draw upon to achieve outcomes that no one firm could achieve alone. Better internal data and pooled industry data, coupled with the power of AI, produces improved processes and better decisions. This approach, for example, would eliminate data reconciliation issues between firms while using AI to resolve any broken trades. Increasingly, capital markets firms understand that these network benefits are best achieved with the help of a trusted industry partner that can leverage industry experience and AI to transform operations while mutualizing their investment.
Despite this opportunity, many capital markets firms struggle to determine how to make a business case for the investment in AI, especially since their data is inadequate. So, AI pilots often fail to roll out in production because of a lack of consistent data at scale. That’s particularly the case in middle- and back-office functions where applying AI in post trade processing depends on an integrated data fabric that consolidates and normalizes data across asset classes, enabling AI to perform complex risk and analysis, and streamlining operational functions.
That may sound complicated, but it simply means that extracting real value from information is predicated on the proper organization, clarity and veracity of data. Only good data produces good results. Information Management magazine highlights the challenge of establishing a robust data fabric, saying it “must support the modernization of storage and data management and move away from the proliferation of data silos. But a data fabric must also integrate with legacy systems, without requiring their presence for the long-term. To work effectively a data fabric must be broad and support a vast array of applications and data types at scale across locations.”
That type of progress, however, requires a consistent data ontology. For example, solving a problem with a broken trade can become very challenging for machine learning algorithms if different firms store data differently. Multiply that problem by hundreds of capital markets firms trading together and the problem grows exponentially.
The greatest business potential of AI cannot be reached from within the four-walls of an organization, it comes by applying AI to improve interactions across a network of industry participants. That journey starts with a common data ontology across asset classes that applies across a network of capital markets firms. Just as a group of people from around the world cannot communicate unless they have a universal translator, leveraging a consistent data fabric across a network of firms enables all participants to truly realize the value of AI when it is applied to redefine business processes across the network.
Capital market executives surveyed by TABB Group say AI is the top technology disruptor and their No. 1 priority. However, capital market firms still face considerable budgetary constraints even after cutting more than $40 billion in costs over the past decade. In addition, many firms are struggling to make a business case to make the required investment while maintaining their straight-through processing systems. They need to mutualize investments and innovation with a strategic partner to realize benefits. This approach also allows firms to spread their bets at a time when there are plenty of emerging technologies. That being the case, many firms want to work with a partner that can help them move their internal experiments withAI, Machine Learning (ML), and Robotic Process Automation (RPA) into large-scale production.
Since leveraging the full benefits of AI/ML/RPA at scale across any capital markets firm will require significant investment that would effectively duplicate the effort of overhauling the same back- and middle-office functions, it makes sense for firms to work with strategic partners. That’s especially true when working with others brings the benefits of the network effect. By teaming up with a trusted technology and managed services leader with deep financial services industry expertise, capital markets firms can transform their operations and mutualize their cost, innovation and investment, giving capital markets firms a cost-effective on-ramp to create a competitive advantage with these next generation technologies.
(Michael Alexander is president of wealth & capital markets solutions at Broadridge, Vijay Mayadas is president of fixed income and analytics at Broadridge and previously led Broadridge’s blockchain strategy.)
Technology has enhanced capabilities of surveilling larger and more disparate data sets.
With Eugene Kanevsky, James Redbourn, and Joanna Wong, CLSA
AI/ML on the buy-side trading desk is a long-term program with short- and mid-term deliverables.
Aiden uses the computational power of deep reinforcement learning to improve trading results and insights.
BondDroid’s AI-generated prices are integrated directly into LTX’s pre-trade analytical tools.