Data mining is top AI priority for financial services firms, says Broadridge survey
The financial services industry’s top priority for AI applications is data mining, according to the second annual AI Outlook Survey from Broadridge Financial Solutions.
The survey, which explores the state of AI adoption and readiness among US financial services companies, reveals that data mining (36 per cent) is followed by post-trade processing (20 per cent), market analytics (13 per cent) and trading systems (12 per cent) in the list of priorities.
Broadridge polled operations, technology and regulatory leaders from across the financial services industry for the survey and released the findings in conjunction with a white paper focused on AI adoption.
Comparing and relating the progress of AI initiatives to relevant historical eras, a clear majority of respondents (84 per cent) say their company is in or past “The Enlightenment Age” of AI, during which they are at or beyond proof of concept. Twenty-nine percent of companies have moved into the “Industrial Age” with pilots and one-fifth (20 per cent) are in the modern “Information Age” with AI in full production.
Though most companies are in some stage of AI adoption, or at least exploration, a cautious 10 per cent remain in the Stone Age with no current plan to leverage AI. Broadridge’s white paper is paired with the AI Readiness Assessment, which helps firms establish the strategy, structure, systems, skills and staff needed to create a successful AI program.
“While most organisations recognise that AI is a transformational technology with huge potential impact, their approach to adoption has been cautious. The survey data and white paper demonstrate how to harness the power of AI and successfully increase its adoption by first establishing a clear strategy and framework,” says Michael Tae (pictured), head of strategy for Broadridge. “At Broadridge, we are focused on what we call ‘the ABCDs of innovation’: AI, blockchain, the Cloud, digital and beyond. This is how we define our continued commitment to driving the innovation roadmap; helping our clients understand and apply next-generation technologies to transform business, optimise efficiency and generate growth.”
Respondents also ranked their top motivations or desired outcomes for investing in AI. Half (53 per cent) cited “increased efficiency and productivity” as their top motivation and a majority (84 per cent) included it in their top three. Other top-three motivations among respondents included enhanced data and security (69 per cent) and the ability to redeploy human capital (51 per cent).
While it’s encouraging that a high number of respondents understand the advantages of AI’s capabilities, roadblocks continue to impede implementation. Nearly half of respondents (46 per cent) cited legacy technology as their top challenge. This tracks with the difficulties associated with modifying or replacing a current infrastructure and the potential need for vendor or personnel changes. Cost of investment/perceived ROI was named the second largest roadblock (31 per cent), while executive buy-in was considered a challenge by only 7 per cent of respondents.
When asked which superhero relationship best describes their company’s interaction with AI technology partners, a majority (58 per cent) chose “The Avengers,” alluding to their desire for teamwork. Conversely, 8 per cent admitted to having an adversarial relationship with their fintech partners, likening it to that of Batman and the Joker. Other respondents compared their technology-related relationships to that of “frenemies” like Professor X and Magneto (16 per cent) and internal conflict like The Hulk’s (14 per cent). The power of partnership for planning smooth AI adoption is clear and prevalent for financial services professionals and technology vendors.
The corresponding Broadridge white paper reveals the four stages of AI adoption — beginner, experimenter, fast follower and innovator — and how they differ from the stages that have played out in previous waves of technology transformation. According to the white paper, AI algorithms are often self-teaching and improve over time, making them opaque and difficult for competitors to copy or reverse engineer. Those in the innovator stage truly have an advantage over fast followers and experimenters.