During a recent AI symposium, research firm Evident convened 200 AI leaders, technologists, academics, and policymakers to discuss the deployment and impact of AI across the banking sector. Throughout each session of the agenda, speakers expressed a shared sense of awe in the momentum, acceleration, and critical mass driving AI efforts throughout the past year.
“If you are really going to have a transformation in your current customer relations, then you’ll need to complement traditional AI with generative AI to drive non-linear growth,” said David Rice, global chief operating officer of Commercial Banking at HSBC.
He added that: “Banks are an information services business, so the possibilities here… the opportunities here… are nearly endless. But also, the level of disruption and transformation. So the opportunities are both endless in one respect, but constrained in terms of how we methodically change our infrastructure to do this at scale. The time to get to mass adoption is how fast you safely scale that infrastructure.”
Foteini Agrafioti, chief science officer at RBC, said: “Just to focus on Generative AI – we do not feel, like many others, that the technology is ready for primetime, if I define primetime as client-facing in the near-term. We don’t expect to see a client interacting with a chatbot to get financial advice in 2024.”
“First of all, you’re not going to build these things [LLMs] yourself – at least, not this year. We’re the only financial services firm in the world with a direct partnership with OpenAI’s research team. Two years ago, that was the only firm with an offering in this space,” said Jeff McMillan, head of Analytics, Data, and Innovation for Morgan Stanley Wealth Management.
He added that: “We didn’t put economic KPIs against early GenAI pilots, because the goal was learning. But there’s meaningful savings for all of us in these tools. It’s not hyperbole. It’s real.”
Evident summarized “the general order of operations” for generative AI: First, we’ll see GenAI use cases deployed to back office and middle office functions (a.k.a. “no regret” applications). Second, we’ll see internal, private chatbots deployed to front-line workers to help speed document discovery and summarization. Third, we’ll see GenAI deployed to accelerate code generation, which speakers frequently stressed should be in tandem with established safety checks and human review.
AI risk and regulation enter mainstream
From the UK AI Safety Summit, to the White House Executive Order to the EU AI Act, a range of concerns have surfaced – spanning long-term existential threats and geopolitical risk to more immediate issues around model bias and surveillance. Meanwhile, the OECD AI Incident Monitor (AIM) is reporting an acceleration of reported harms from AI, providing compelling evidence that effective regulation is not only necessary, but essential.
“Banks are on the receiving end. We are not managing our regulators and supervisors, but we can engage with them and explain the technology – especially where there is broad consensus on the principles of AI,” said Stefan Simon, CAO and head of the Americas at Deutsche Bank.
“Regulation will become a testing goal – same as for food or drugs. The business will tell us when the outcome is wrong and we will re-do, re-test, get more data, get more input… until the AI passes the test,” said Manuela Veloso, head of AI Research, at J.P. Morgan.
Widening gap between leaders and laggards
Only three banks (J.P. Morgan, Capital One, and Royal Bank of Canada) score >50 of 100 points in the Evident AI Index – establishing a decisive lead over the rest of the industry in terms of their relative AI maturity. Going forward, identifying unique, proprietary use cases specific to each bank (versus what’s defined as “table stakes” for the wider industry) will become critical in ongoing communications with investors, customers, and prospective hires.
“Where do you have data that is separate and differentiated? Where do you have data that’s different from everyone else – and how do you use the machine to express that advantage? It’s a different way of thinking about the world,” said George Lee, co-head of the Office of Applied Innovation at Goldman Sachs.
“Because of the open-source origin of many AI technologies, there is little competitive advantage in the underlying tech – the competitive advantage resides in your proprietary data. Data is such a valuable asset, it should be on your balance sheet,” said HSBC’s Rice.
“The last 20 years were about algorithms. It’s going back to data and to capitalize on that data. Banks are in a good position here. I think the big difference here is there are markets… where the second mouse gets the cheese. It’s not so much about being first with something, because you can protect it,” said Vahe Andonians, CTO, CPO and founder of Cognaize, a firm that provides intelligent document processing.
ChatGPT – one year on
Andrea Seminara, CEO of Redhedge, said in an emailed statement to Finadium: “While AI has brought about significant changes in various aspects of our lives, its impact on financial markets, particularly in areas such as market making and investment strategies, remains supporting rather than leading. AI assists us in analyzing intricate data and identifying patterns, but the dynamic and unpredictable nature of financial markets often surpasses even the most advanced AI algorithms. This highlights the invaluable significance of human intuition and expertise in making financial decisions.
“Over the past year, generative AI has indeed made significant inroads into the business sector. Although it hasn’t yet transformed financial markets, its advancements have been particularly notable in applications such as translations, writing assistance, and data analysis. These areas have benefited from the technology’s rapid development and increasing adoption across various industries.
“As we acknowledge the progress made in AI, it is crucial to recognize the importance of combining these technological marvels with the profound understanding and experience of human beings. It is in this collaboration that the true potential of AI in finance can be realized.”
Gudmundur Kristjansson, founder and CEO of Lucinity, an Icelandic anti-money laundering (AML) compliance platform for fintechs and banks, said in an emailed statement: “Lucinity data shows that the recent advancements in generative AI and copilot technology can save the banking industry $27 billion annually in training and staff costs. Tier 1 banks have the potential to achieve annual savings ranging from $15 million to $36 million on training and recruitment expenses.” said
“The introduction of ChatGPT has addressed the pressing need to upskill workforces in the financial services sector. By leveraging the power of AI, we have witnessed a remarkable increase in skill levels and output quality by up to 40% while simultaneously reducing training costs by millions of dollars annually per bank.
“The impact of ChatGPT goes beyond mere cost savings. It has revolutionized the way financial institutions approach financial crime prevention. By partnering human expertise with AI capabilities, we have created a symbiotic relationship that elevates the entire workforce. This partnership is not about replacing individuals but rather about empowering them to become more effective and efficient in their roles.
“Our AI copilot, Luci, powered by ChatGPT, has been instrumental in transforming everyday analysts into superhero investigators. With Luci’s user-friendly interface and instant rewards, compliance teams have experienced a significant boost in learning speed and efficiency. This has allowed them to quickly adapt and develop into more valuable case reviewers and investigators.
3/4 of banking execs believe GenAI will significantly impact sector
75% of banking executives believe their industry will be significantly impacted by Generative AI, according to the Economist Impact study, commissioned by Temenos, which surveyed 300 bank executives from across five continents.
Nearly one in three jobs (30%) now being advertised in the European banking sector mention AI, according to the report. Banks are also using AI and machine learning to better understand consumers and more effectively deliver personalized, more efficient banking experiences.
The most common customer-facing AI tools being developed are chatbots – with the likes of Bank of America’s virtual financial assistant, Erica, being used by more than 37 million customers in over 1.5 billion interactions between 2018 and 2023. Research has found that banking chatbots – offering personalized experiences to customers – will also save banks $7.3 billion in operational costs in 2023.
Temenos has its own GenAI solution, which enables banks to classify and label customer transactions from free text narrative automatically, with a high degree of accuracy in different languages.
Hani Hagras, chief science officer at Temenos, said in a statement: “The industry is now coming to terms with the significant technological changes that AI brings, and the opportunities it provides for the growth of the global banking sector. We are entering a new era where Generative and Explainable AI will have the potential to change the face of banking for the better by enabling new business opportunities and services, as well as improved support for the banks and their customers.”