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How to make the latest tech work for you

APPLYING CUTTING-EDGE TECH TO CAPITAL MARKETS

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Up until recently, the application of advanced technology to the post-trade portion of capital markets has added up to little more than an incremental efficiency play, meant to reduce costs and streamline back-office processes. With that approach – and in a heavily regulated industry with decades of legacy technology and practices – it’s understandable that the application of advanced tech hasn’t quite lived up to the hype. But you can’t capture the full promise of advanced tech if you don’t start in the right place. The foundation of advanced technology lies in the collection and use of data – used for everything from automatically improving the user experience to revealing key insights hidden from human view. It’s no wonder that data is called the new “oil” of markets. However, given the restrictions on the use of some data, the dispersed technological landscape most firms operate in today and the newness of the technology, it can be extremely challenging to take the first steps forward. Yet, the possibilities are endless if you build a solid data foundation, embrace new ways of developing systems and commit the investment needed.

Tech Under the Microscope

A different way of working

At the most basic level, AI enables a machine to imitate human behavior, learn from it and improve on it, so that humans can stop wasting time chasing the details. Instead, they can make the important calls, and begin to exercise the kind of game-changing creativity that only humans are capable of mastering. Unlike other technologies that perform only one function within a defined role, AI is malleable. It can be adapted easily to solve a variety of problems, based on the various information you have to feed it. Internal or external data, it doesn’t matter.

If you can get your hands on the data, you can build an intelligent robot to process and report on in any format that suits your need.

Begin at the beginning

Tech under the microscope

Innovation working for you

Why are capital market firms investing in artificial intelligence/machine learning over the next 12 months?

Perhaps the most intimidating aspect of using cutting-edge technology is the enormity of it. The word “modernization” alone is enough to freeze you in your tracks. Rather than boiling the ocean, pick a spot and start there. Focus on one atomic use case that can make a difference. Cash management, securities lending or AML are all good starting points.

Think strategically

Round up the data necessary to solve that problem, determine a success marker based on the system you use today and build a proof-of-concept to test your case. As you refine your model, you will soon evolve an application that supports your business. Then you can begin connecting the dots to the next application in the value chain, leading to a growing platform of cutting-edge solutions. Remember, AI is flexible. The models you build today can be reused and refined in other areas, so any investment you make in the process is a long-term benefit to the enterprise.

The first task in dealing with AI is to examine your data value chain. With data trapped in so many places in and around the organization, you’ll need to determine where your most valuable information is stored and how you can readily access it. Fortunately, modern cloud storage and enterprise infrastructure have matured to a point where accessing vast pools of data has become routine. Vendors can assist in the effort, providing linkages to support your AI model and feed it with essential inputs. What’s important to consider as you move forward is the economic benefit you hope to derive from your efforts. Most AI applications in the capital markets space will be augmenting or replacing rules-based systems, such as anti-money laundering (AML) or credit reserve calculators. If AI can improve these systems by just 20%, it’s an economic gain. Start by determining your baseline. Remember, your data can come from a variety of sources – transactional histories, commodity prices, SEC reports, sensory data, weather bureau information, even leadership changes reported in the news. Find a way to bridge the gap between that data and your AI, and develop the applications you need to achieve your desired outcomes.

Are your terms and conditions clear?

Begin at the beginning

“Any sufficiently advanced technology is indistinguishable from magic.”

Any change in your business process will demand a change in your workforce mindset. This is where change management comes in. When people suddenly go from reactive mode to predictive, new skillsets must be learned. The fear of losing your job to a robot is real. But if people are engaged in the process, they can weigh in on what parts of their job might be improved through AI and see first-hand the value of it in terms of personal productivity and outcomes. People tend to think of AI as a black-box computer, working behind a veil, spitting forth outputs from thin air. Magic that can’t be explained is not necessarily good in an industry that is so regulated and routinely audited. Firms must be able to explain how their systems arrived at their conclusions and demonstrate the linkages behind the technology that produced the results. Explainable AI (XAI) provides the insight needed. XAI is artificial intelligence that can be understood by humans. In a regulated environment, XAI is essential for humans to have confidence in the results. The algorithms that go into it must be explicable. The data lineage, feature lineage and model lineage must all be clearly understood. Indeed, the system’s transparency, interpretability and explicability must be drawn from an end-user perspective if the information produced is to be trusted.

Address the concerns

WHAT IF YOU DIDN’T HAVE TO CHOOSE BETWEEN COST AND INNOVATION?

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"That’s a huge payback that you won’t want to miss."

John Omahen Head of Product Management for Securities Processing, FIS

John Omahen, Head of Product Management for Securities Processing, FIS

Cutting-edge technology in capital markets has become much more than technologists taking solutions to market. It involves leadership, compliance, risk management, IT and operations working together to change how organizations work, think, innovate, manage and interact. In 2020, FIS® began a journey in partnership with C3 AI, a leading enterprise AI software provider to help accelerate the adoption of machine learning technology within FIS. This partnership, along with an investment in dedicated data science professionals has allowed FIS to make significant advances in applying machine learning and AI techniques to its capital markets solutions.

Think strategically

By documenting the mechanics behind a model, AI developers at financial firms can create models that are reliable and defensible – not only to auditors, but also to analysts who might eschew the results as “magic” and carry on in traditional fashion – only to arrive at the same conclusion. So, the discussion comes full circle to change management, wherein users must be engaged in the process upfront. IT can no longer be the sole proprietor of AI solutions. They must be developed under the guidance of a cross-functional team, including IT, data, automation, business and operational principals. Then the resulting applications can be explained and users can sleep at night knowing that their jobs are not only secure, but greatly enhanced by a machine that can do the dirty work 10 times faster and cheaper, while they perform more creative work.

Analyze large data sets

35%

Build new data-driven products and services

29%

Address the concerns

Imagine you have $2 billion in inventory and it improves by

20%

with AI

Combine alternative data sets and artificial intelligence

31%

Enhance process automation and operational efficiency

33%

Provide more meaningful information clients

29%

Strengthen compliance and risk management

37%

Arthur C. Clarke

The Securities Processing Suite provides a global, real-time view of your operations, with AI-driven risk analysis, digital remote access and simplified integrations. With FIS, you can achieve operational efficiencies, improve risk management and better serve your customers. We’ve taken our best-of-class products into the digital age with operational dashboards, advisor portals and mobile capabilities.

With constant investment in our solutions, FIS makes innovation work for you.

An incremental approach is always best. Find the use cases that stand out in your organization, build models to address them, and take care to monitor and adjust those models over time. AI is anything but static. Through continued course corrections, you can continue to evolve your applications to a high degree of performance.

“With great power comes great responsibility.”

Peter Parker - Spiderman

It all comes down to explicability. If your counsel agrees that you are within the limits of ethical behavior and your cross-functional development team has constructed the application around the XAI principles of transparency, interpretability and explicability, you should be able to connect the dots and justify your case. As long as you can explain why the model came to its conclusion, you should have confidence in your final outcome.

AI certainly has the power to drastically change the way financial services are conducted. Because of this, it should be treated with great respect. The ethical issues that arise when you start talking about using personal financial information to manipulate outcomes will cause regulators and society to take note. So, be aware of the risks. If you are using personal information to populate onboarding documents, for example, the risk is probably low that you will cross an ethical boundary. But if you are citing fraud or using personal data to make hiring decisions, gray areas begin to arise.

The ethical perspective

Provide more meaningful information clients

29%

Build new data-driven products and services

29%

Combine alternative data sets and artificial intelligence

31%

Enhance process automation and operational efficiency

33%

Analyze large data sets

35%

Strengthen compliance and risk management

37%

*The 2021 FIS Readiness Report

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