At its core, AI is about data and scale
Different technologies are designed to solve different challenges. In simple terms, AI is concerned with “thinking,” while RPA is associated with “doing.” Here, we’ll use AI to describe anything that helps machines to exhibit intelligent behavior and simulate human intelligence. RPA, on the other hand, is a software designed to mimic human actions.
In both cases, size matters. When you operate at scale, you process more data—which enables machines to detect patterns and learn faster. Capital markets is the most data-sensitive segment of the financial industry—and one of the largest spenders on AI and RPA technology.
The transformative power of AI is only now beginning to emerge
In many cases, banks are working with leading Fintechs who can mutualize key functions to achieve scale at lower costs. Mutualization not only provides the ability to take advantage of leading-edge technology without the full investment—it also provides the advantage of more data running through it.
Initially, the primary driver of AI and RPA adoption was cost reduction and efficiency. RPA, for example, can replace keystrokes, perform specified actions, or trigger process activities downstream from a given event. RPA can also communicate across multiple underlying applications. But these applications may just be scratching the surface of AI’s capabilities.
In many situations, AI and RPA will not replace humans but rather augment them with decision support. More recent use cases go beyond simple automation to identify trade patterns, uncover natural counterparties, and predict pricing.
Every Day, Firms Turn to AI to Remedy Distinct Pockets of Inefficiency
- Anti-Money Laundering
- Asset Allocation
- Chatbots
- Clearing
- Corporate Actions
- Know Your Customer
- Reconciliation
- Risk Analytics
- Settlement
- Trade Allocation
- Trade Monitoring
Continuous improvement can get you from good to great
Many initially look at AI as a way to streamline manual processes, cut costs and generate back-office efficiencies. However, the more significant AI opportunity lies in harnessing data to generate insights that drive enterprise value.
Three recent Capital Markets initiatives highlight the range of AI and RPA benefits:
- Global Trade Allocations
Requests for trade allocations arrive in many forms: emails, PDFs, Excel files, screenshots. The first step is converting unstructured data into a structured format using Natural Language Processing and Optical Character Recognition technologies. Machine Learning then identifies patterns using historical data sets. With all input now digitized and standardized, allocations can be processed automatically. - Enterprise Reconciliation
Every process relies on data, so companies have made data reconciliation a priority. Match rates are generally high, and most firms have set up a Center of Excellence or shared service to manage exceptions. The problems with those are twofold. First, as the volume of data increases, the need for additional headcount rises. Second, when transaction volumes spike, teams have trouble keeping up.
AI adds a smart layer of control to reconciliation and break-management processes. Machine learning algorithms automatically generate and improve matching rules, classify matching problems, and suggest resolution steps. The result: Fewer manual investigations, faster resolutions, and more time for mission-critical activity. This is a perfect example of where size and scale matter. The Broadridge Intelligent Automation solution, for example, is continually running champion/challenger tests to identify and validate better ways to match and resolve exceptions. - Fixed Income Trading
It is no secret that the corporate bond market is hugely complex, inefficient, and illiquid. Only about 25% of U.S, high-yield corporate bonds are traded electronically. As the industry migrates to more digital, Cloud-based solutions , AI is playing a critical role, helping broker-dealers identify natural counterparties using both real-time and historical trading data. The data may suggest counterparties that may not be in the “usual suspects” the dealer typically calls. By engaging these less obvious buyers and sellers, firms can execute trades with speed and efficiency—putting themselves in a position to deliver the ultimate best execution.
Be ready today and tomorrow
More than 95% of financial firms see value in co-developing AI with other firms and partners. Going forward, you can expect firms to rely on AI to tackle an even broader range of challenges:
- Increase efficiency: Layering AI and cognitive capabilities on automation technologies to enable self-learning and increase autonomy.
- Greater personalization: Delivering superior client experiences through hyper-personalization, conversational interfaces, and the curation of real-time information.
- New products: Using AI to introduce new products and services—tapping into new business models and markets.
Broadridge works across a large network of clients, giving us the scale to provide AI solutions that companies cannot provide on their own. Every day, we simplify the complex with The ABCDs of Innovation®. It’s how we help our clients understand and apply next-gen technologies—including AI, blockchain, the Cloud and digital—to transform their business and get ready for what’s next.
