The trading world is constantly evolving, with traders having to adapt to the latest trends and use the latest technology to ensure they can keep up with the volatile market: AI is slowly becoming a necessity to find success in the market. NVIDIA conducted a survey named the “State of AI in Financial Services” that looked to see how and why AI was becoming so important. The 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.
John Harding is Regional Director, UK & Ireland, at NVIDIA. Harding is passionate about global financial services and fintech and he leads the sales and developer teams responsible for growing the adoption of NVIDIA AI platforms across industry and the public sector. He looked at NVIDIA’s findings that showed 83% of global financial services professions agree that ‘AI is important to my companies future’s success’, and 34% of respondents stated AI will increase their annual revenue by 20% or more”, and in turn discussed how AI was found to have the largest impact on yielding more accurate models, creating a competitive advantage, developing new products and improving operational efficiencies:
While stories about the seismic shift toward digital banking in the post-covid era abound, another technology revolution is taking place in capital markets: 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 utilise 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. According to NVIDIA’s “State of AI in Financial Services” report, 83% of global financial services professionals agreed with the statement that “AI is important to my company’s future success.”
The impact of AI on financial markets is real and measurable, with 34% of respondents stating that AI will increase their company’s annual revenue by 20% or more. Across the broader financial services landscape, 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.
For investment firms, algorithmic trading and portfolio optimisation 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 on investment firms, what is holding them back from achieving their AI objectives? The biggest challenges are too few data scientists (38%), insufficient technology infrastructure (35%) and a lack of data (35%). These challenges are all related, as it turns out.
Finding and retaining top talent is a challenge for any part of an organisation and that’s certainly the case in AI. The C-suite can overcome this by infusing AI expertise across the organisation. 60% 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 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. 95% of respondents are planning to increase their 2021 investment into AI infrastructure, with 44% saying they’ll do so by more than 10%.
Lack of data can sometimes be addressed with creative thinking — how can I transform or buy data that would turn what I do have into something more valuable? In other cases, it’s a timing problem — we don’t have enough data now, but if we begin an AI journey, we could create a cycle where the more data we have, the more value our models deliver, making the return on investment in managing the additional data higher, and so on. Access to data can be solved by a combination of data scientists and infrastructure!
Putting it Together
Regardless of how a trader is using AI, investment firms must employ an enterprise AI strategy that creates a competitive advantage in the market.
C-suite and IT leadership at investment firms are challenged to build enterprise-level AI platforms that can scale and deliver productivity, and return on investments to support the AI professionals within their companies. Initially, financial institutions will need to proactively elevate AI as a strategic imperative for it to become a core capability.
The same opportunity exists within commercial and retail banks. Rather than relegate AI to the “research lab,” the banks that are creating meaningful impact with AI are developing strategic plans, resourcing teams appropriately and establishing an AI infrastructure platform. Banks 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 amongst investment firms to enable portfolio managers and traders with AI. As data continues to proliferate across a variety of channels and dimensions, it’s no longer just the owner of the data who holds the keys. Those who can uncover actionable insights that create competitive advantage will lead the industry to the “Trader of Tomorrow.”
