Generative AI’s role in the evolution of banking

Generative AI presents a significant revenue opportunity for banks, not just as a cost-saving tool. 

Banking has changed a lot, especially how we, as consumers, interact with banks. Digital banking, for instance, has brought a layer of convenience where we can easily check balances and move money across accounts or banks. Yet, despite the rapid digitalisation, no single digital-only bank has truly hit scale as traditional banks continue to dominate. 

Then came generative AI, which took the world by storm. The rates of adoption and enhancement are more rapid than any major technology innovation in the history of human development. Due to the importance of language throughout the value chain, the banking industry has a greater potential to benefit from the technology than any other. Accenture’s research found that globally, 67% of all working hours in banking will be impacted by generative AI. 

Perhaps generative AI will not fundamentally change the basics of banking – collecting and safeguarding deposits and lending money – but it could be the most disruptive technology the banking industry has seen in decades and will change how banking is delivered. 

The revenue opportunity for banks
Generative AI presents a significant revenue opportunity for banks, contrary to the common perception of it being primarily a cost-saving tool. According to a new Accenture study, generative AI could boost bank revenues by 600 basis points in the next three years, increase return on equity by 300 basis points and grow operating income by around 20%. These are figures and upsides that no bank can afford to ignore. 

There are hundreds of potential use cases and applications across various banking functions, from risk management to customer service. For instance, generative AI is already transforming anti-money laundering and know-your-customer practices. Its coding applications are some of the most exciting and range from the ability to reverse engineer decades of spaghetti COBOL code to developing new digital customer experiences at unprecedented speeds. 

Moreover, generative AI has the potential to raise the bar for personalisation while helping reps solve customer inquiries faster. Its real power will be in augmenting workers. For example, imagine a relationship manager who, during a client conversation, can look at a navigation map-like guide. If the conversation is “red,” with the customer showing signs of disinterest, the system could provide them with a detour and prevent them from heading down dead ends and detours in the conversation.  

It’s instructive to compare the immediate impact of generative AI with other recent technologies. When blockchain and the metaverse emerged, there was much discussion about how the technologies could change banking. With gen AI, we’ve already seen banks come up with thousands of opportunities. The challenge isn’t what you do, it’s what you decide not to do, reinforcing the belief that this is a true paradigm shift.

It’s like going from the slide rule to the calculator. In fact, a majority of banking leaders (71%), according to a recent Accenture study, point to generative AI as a key lever in their continuous reinvention strategy and two-thirds (66%) see the technology as more of an opportunity than a threat. 

The last year has seen staggering adoption and advancements in maturity, with banks moving beyond proof of concepts to use cases. Not surprisingly, banks that have already invested in AI and analytics and that have strong digital cores are leading the way in generative AI. Thanks to cloud-based services, much of the technology around generative AI can be purchased on a credit card. It just requires the right time and focus.

The next step in generative AI’s evolution at banks will be establishing the necessary infrastructure to support the adoption and implementation within the organisation, ensuring  security, risk management and responsible AI practices are integrated into systems and processes.

Singapore’s central bank and financial regulatory authority, Monetary Authority of Singapore (MAS), for instance, established the consortium Veritas, a collaborative initiative to enable financial institutions to evaluate their AI-driven solutions on principles of fairness, ethics, accountability, and transparency (FEAT) to strengthen internal governance around the application of AI and the management and use of data. The consortium's work is highly regarded globally, often used as a benchmark by other regulators. Model risk management will be critical as banks navigate the proliferation of large language models and customise existing or build their own models. 

Reinvent talent and ways of working
Generative AI's widespread impact across all aspects of banking requires a comprehensive reinvention of processes and roles within banks. You can’t go out and hire someone with five years of generative AI experience. They don’t exist. 

This reality underscores the need for a cultural shift toward embracing change and innovation to effectively adopt and scale the technology. A forward-thinking culture if crucial rather than benchmarking against what competitors were doing years ago. 

As the nature of talent and bank roles could change dramatically, banks must consider how their talent moves around in different ways across the bank and embrace skills-based HR. 

While generative AI will not completely disrupt the fundamental nature of banking, it will be a driving force for continuous reinvention, making banking infinitely more meaningful for employees, customers and investors. The technology can free employees from process-orientated work, enabling them to focus more on customer-centric activities. For the end customer, this translates to a faster, more personalised and seamless experience from their bank.