The pace of commercial AI innovation over the past 12 months has been remarkable and nowhere has progress been more impressive than in the financial services sector.
At a recent AI Symposium, Microsoft’s UK CEO Clare Barclay remarked that, in terms of overall AI maturity, “I’ve seen, if anything, the most progression in thinking, adoption, strategy and piloting in Financial Services… The challenge remains turning a proof-of-concept into production.”
Her comments neatly encapsulate the state of play for banking and financial services institutions alike. Giant strides forward have been made, yet the question everyone is grappling with is how to deploy potentially transformative AI use cases at scale in a way that maximises ROI and mitigates any potential negative consequences?
As each organisation plots its own unique course towards a viable AI production line, the overall technology landscape within financial services is going to shift dramatically in the months ahead. Here are eight key trends likely to play out in the race for AI supremacy in 2024.
#1: The gap between AI leaders and laggards will widen
Today, three banks – JPMorgan Chase, Capital One, and Royal Bank of Canada – hold a decisive lead over the rest of the industry in terms of their relative AI maturity. While I expect to see widespread urgency across the field, few banks are currently equipped with the foundational elements to accelerate as fast as they would like – or at the continuing pace set by these industry leaders.
#2: AI will lead to more consolidation in banking
The US banking market has given US institutions a clear first-mover advantage with respect to AI maturity, thanks to its capital resources, proximity to Big Tech, and a supportive regulatory environment. However, this advantage is not evenly distributed, with a stark contrast emerging between the top AI performers and the rest of the market.
In a market characterised by a fierce war for talent, expertise and IP, this environment poses an existential threat to smaller banks with little choice but to rent or buy (versus build) their AI future. We’ve already seen consolidation across the global banking system over the past 12 months, and it seems a likely bet that AI will inspire a further wave of consolidation, particularly within the bifurcated US market.
#3: As the AI talent war intensifies, so will banks’ efforts to reskill existing employees
The demand for AI talent within the banking sector is surging – our research showed a 10% increase in AI talent across the world’s biggest banks from May to September 2023, despite a 2.5% reduction in overall headcount.
However, employees with AI-relevant skills have a wealth of opportunities open to them, most notably with Big Tech. Poaching will be rife in 2024, and so in addition to bringing in new talent, I expect to see banks doing everything they can to reskill existing employees and ensure AI-focused training and career development opportunities are available across all levels of their organisation.
#4: Banks’ GenAI usage may be limited to ‘no regret’ applications…
We’ll see GenAI use cases deployed to back office and middle office functions (a.k.a. “no regret” applications) in the near future as banks look to harness this breakthrough technology. We’ll also see internal, private chatbots deployed to frontline workers to help speed document discovery and summarization, as well as GenAI deployed to accelerate code generation, where it can be safely overseen. Other potential use cases, however, such as risk modelling of liquidity positions, remain a long way out, and we shouldn’t expect to see these in 2024.
#5: …But lagging institutions can still use GenAI to get back in the game
The rapid advancement in GenAI has democratised access to AI tools like never before. While it is only a subset of the AI toolkit, GenAI nevertheless provides an opportunity for banks that haven’t invested as significantly in their AI workforce to get capabilities into the hands of teams across the bank quickly. In 2024, GenAI will be a critical tool within lagging institutions, helping them drive bank-wide ideation, increase AI literacy, support cross-organisation knowledge-sharing amongst AI talent, and, ultimately, regain ground in the race towards AI maturity.
#6: Some smaller banks will surpass many industry leaders for AI maturity
The fact that smaller institutions such as CommBank and DBS rank in the global top 10 banks for AI maturity demonstrates that resources alone do not determine AI success. From establishing Centres of Excellence and building partnerships with Big Tech and academic institutions, to reorganising decision-making processes to get the time to AI production down, AI-first challenger banks pose a legitimate threat to any of their supertanker-sized rivals that struggle to step up the pace of AI progress in 2024.
#7: AI leaders will increase their presence on operating committees
Over the next 12 months I expect to see more banks acknowledge the importance of AI at the executive level, either through existing team members driving the bank’s AI agenda, or by appointing new Operating Committee members with a dedicated AI/Data remit.
Thus far there are only three instances across the world’s biggest banks of formal AI/Data representation at an Operating and/or Executive Committee level (JPMorgan Chase, NAB, and CaixaBank). This simple, but impactful change to executive leadership teams has newfound urgency now that AI production lines are the priority. Banking boards need to understand the technology they’re being tasked to govern.
#8: Banks will shift focus from ‘What can AI do?’ to ‘What ROI does AI create?’
Over the past year, several banks have already seen a stock price bump based on their perceived affinity with AI. As markets gain understanding of AI use cases, however, more and more scrutiny will be brought to bear on persistent questions regarding ROI. How does your AI strategy differ from the immediate competition? How much is your bank investing in AI? What initial returns are you seeing from these projects? In 2024, there will be a definite shift from experimentation to monetisation, with banks attempting to standardise how business use cases get evaluated and measured.
