By: John Harding, Regional Director, UK & Ireland, NVIDIA
While stories about the seismic shift toward digital banking in the post-COVID era abound, another technology revolution is taking place in capital markets: the era of AI-powered trading. Recent market fluctuations and the impact of social media sentiment on stock prices have highlighted the need for active fund managers, traders and market makers to utilize AI in order to compete effectively in the future.
These trends are apparent in some of the findings from NVIDIA’s recent survey of financial services professionals from around the world. The “State of AI in Financial Services” survey consisted of questions covering a range of AI topics, such as deployment models, infrastructure spending, top use cases and biggest challenges. Respondents included C-suite leaders, managers, developers and IT architects from fintechs, investment firms and retail banks.
In fact, according to NVIDIA’s recently released “State of AI in Financial Services” survey report, 83% of global financial services professionals agreed with the statement that “AI is important to my company’s future success.”
Diving into that statement, the impact of AI on financial markets is real and measurable. According to our survey, 34 percent of respondents state that AI will increase their company’s annual revenue by 20 percent or more. Across the broader financial services landscape, survey respondents identified four key areas where AI is impacting their company today: yielding more accurate models, creating a competitive advantage, developing new products and improving operational efficiencies. AI is growing revenue and market share while shrinking costs across the industry.
Specifically, for investment firms, algorithmic trading and portfolio optimization were identified as the most common class of AI applications. Every trading decision — what to buy or sell, at what prices, when and where to execute trades — can benefit from either AI powered algorithms or from systems that augment human decision makers with AI-powered assistants.
Roadblocks to Achieving AI Goals
Given the significant impact of AI to investment firms, what is holding them back from achieving their AI objectives? The biggest challenges to achieving AI goals are too few data scientists (38 percent), insufficient technology infrastructure (35 percent) and a lack of data (35 percent). These challenges are all related, as it turns out.
Finding and retaining top talent is a challenge for any part of an organization and that’s certainly the case in AI. However, the C-suite can overcome these by infusing AI expertise across the organization. 60 percent of C-level executives responded that their largest focus moving forward is identifying additional AI use cases — driving the demand for even more data scientists. One in two respondents from the C-suite noted that their company also plans to hire more AI experts — directly trying to addressing the gap of too few data scientists.
The technical infrastructure for AI has never been more available, although the plethora of choices (and existing “shadow IT” investments) can sometimes make it feel like an overwhelming problem. Whether on-premise, in the cloud, or in a hybrid environment, container-based, GPU accelerated systems can be rapidly built and deployed. This too is a soluble problem.
Lack of data can sometimes be addressed with some creative thinking (how can I transform or buy data that would turn what I do have into something with more value). In other cases, it’s a timing problem — we don’t have enough data now, but if we started on an AI journey we could bootstrap ourselves onto a virtuous cycle where the more data we have, the more value our models deliver, which makes the return on investment in managing the additional data higher, and so on. Key to solving this problem is a combination of data scientists and infrastructure!
Putting it Together
Regardless of whether a trader is managing a portfolio of algorithmic trading models or utilizes insights from AI to drive discretionary trades, investment firms must employ an enterprise AI strategy that creates a competitive advantage that otherwise will be captured by the competition.
C-suite and IT leadership at investment firms are challenged to build enterprise-level AI platforms to scale and deliver productivity and return on investments to support the growing AI professionals across their companies. As a starting place, financial institutions need to proactively elevate AI as a strategic imperative to the firm that needs to ultimately become a core competency.
The same opportunity exists within commercial and retail banks. Rather than relegate AI to the “research lab,” the banks that are creating meaningful impact from AI are developing strategic plans, resourcing the teams appropriately and establishing an AI infrastructure platform upon which the bank can productively scale dozens if not hundreds of AI applications and see a significant return on investment.
Enabling the “Trader of Tomorrow”
The race is on among investment firms to AI-enable portfolio managers and traders, among other roles and functions. As data continues to proliferate across all variety of channels and dimensions, it’s no longer just the owner of the data who holds the keys, but the ones who can uncover actionable insights to create competitive advantage from all varieties of data will lead the industry to the “Trader of Tomorrow.”
The “State of AI in Financial Services” survey consisted of questions covering a range of AI topics, such as deployment models, infrastructure spending, top use cases and biggest challenges. Respondents included C-suite leaders, managers, developers and IT architects from fintechs, investment firms and retail banks.
