Innovation through convergence

Technology and data

Since the financial crisis, cost pressure continues to push financial institutions to do more with less.

Over the last decade, sell-side firms have been dealing with competition from fintech entrants, low interest rates, and a shift from actively managed funds to passive funds, all of which are squeezing margins. At the same time, post-crisis regulations are increasing reporting requirements.

Against this changing landscape, sell-side firms need to cultivate an edge. Data and technology are becoming key to how sell-side firms differentiate and will continue to be important over the coming years.

Firms are still figuring out the most valuable applications. Sell-side firms are using technology to improve risk governance and oversight on front-office desks. Still others are exploring technology that helps them understand and visualize the plethora of data they now have – whether it’s analytics about risk, pricing patterns, or numerous other potential applications.

Once viewed as a cost center, technology is adding value to the business in new ways. Crunching data can help sell-side firms find savings or keep costs down. Real-time risk calculations can help match buyers and sellers automatically so that it does not impact the sell side’s balance sheet. Firms are also looking to consolidate systems across desks, improving efficiency while lowering the cost of ownership. For instance, front-office trading desks can use a consolidated system for pre-trade idea-generation, pricing and structuring trades or to monitor intra-day and end-of-day risk, all on a single platform.

Implementing some of these new applications requires massive investment in infrastructure, automation, machine learning, and staff training. It also means having to rethink technology stacks to create more integrated workflows across departments.

Consequences of late adoption

Compared to other industries, finance was slightly late to adopt and apply new technology. Twitter,

Amazon AWS, and Hadoop all came out around 2007, right before the financial crisis. At that time, the sell side did not look to apply these new technologies.

“Spreads were wider, we were all doing well, and there was not much incentive to look at these new technologies,” said Naz Quadri, Bloomberg’s Head of Machine Learning and Alt Data Practice.

That has changed. Big tech players are taking early steps into financial services. New entrants aren’t just creating competition, they’re also disrupting the industry.

New technology is enabling entrants to serve customers in new ways, faster, and with less capital. The average cost for traditional banks to acquire a new current account customer is $150. For a digital challenger bank, it’s only $30 because of its technology stack. Customers at challenger banks can apply for and start using a new account the same day, while it takes an average of three days at a traditional bank, according to the Aite Group.

The sell side is now seeing how technology can enable new ways to bring products and services to market. At Bloomberg’s 2019 Sell Side Enterprise Forum in London, 78% of attendees said they plan to invest in greater automation in pricing or execution over the next five years. And 43% said their firm is in the early stages of adopting AI through some small pilot projects.

Roadblocks to implementation

With the amount of data increasing exponentially, sell-side firms are faced with the technical challenge of gathering data from different parts of the business, accurately and quickly. Bloomberg

is now handling over 100 billion messages and 1.5 million stories daily. To handle such massive amounts of data, firms are augmenting legacy infrastructure systems with cloud computing. However, connecting these different systems seamlessly remains a challenge.

Because sell-side firms are relatively early in their adoption of AI, machine learning, data analysis, and other new technology, many lack seasoned expertise in these areas. About a quarter of attendees at the 2019 Bloomberg Sell Side Enterprise Forum said that internal knowledge was the biggest roadblock for adopting AI and machine learning, followed closely by management comprehension.

Firms are learning to compensate for that lack of knowledge in a number of ways. One common approach is to invest in fintech and tech startups or by buying firms. Fintech investment in the U.S. reached $21 billion in the first six months of 2019, while worldwide investments totaled $37.9 billion, according to KPMG. While this was a dip compared to the year before, it was mostly

because 2018 figures were propped up by a megadeal. Payments have been a hot area of investment; others include regtech and cybersecurity.

Firms are also partnering with vendors to make up for lack of internal expertise. Banks have been partnering with fintech or tech firms to add new capabilities that would be difficult or cost prohibitive to develop internally. Buying from vendors gives firms a chance to implement and scale-up new technology that’s in line with others in the sector. Firms can then build alpha on top of that. To put it another way, clients can buy beta, but need to build alpha.

The other big roadblock for more widespread adoption of machine learning and artificial intelligence is interpretability and explainability. Using artificial intelligence, sell-side firms can analyze large amounts of data in novel and exciting ways. But understanding the findings — let alone explaining them — can be challenging. Customers and regulators want more transparency and reassurance before AI can be adopted more widely.

While regulators have some guidelines in place regarding interpretability, there are no specific requirements yet. Regulators don’t necessarily need all the details but are looking for assurance that the end client understands enough and feels comfortable. Sometimes explaining the intention of the algorithm is enough to give reassurance that it’s not just a black box.

Looking ahead

While sell-side firms are busy investing in new technology, the industry as a whole is still working out how to monetize and use that new tech as a catalyst for transformation. Merely having new technology does not intrinsically give firms the edge they need; today's traders need to better leverage data to optimize high-touch and no-touch trading.

The fast pace of change has sparked some concerns around the longevity of the alpha firms can produce. An innovation, whether it’s multi-curve swap pricing or artificial intelligence, gives an edge for a while. But that edge diminishes as soon as others catch up.

Staying ahead is never easy, but sell-side firms that take the time to truly understand client needs, acknowledge internal shortcomings, and find long-term technology partners are on the right track.