A version of this article was originally published in The Business Times.
One year ago, OpenAI launched ChatGPT, which captivated the world with its ability to produce remarkably humanlike text responses. The event ignited a surge of interest in generative artificial intelligence (AI), prompting organizations to explore how the technology could transform their operations. Banking executives in particular have come under pressure to incorporate generative AI into business processes. Already, despite skepticism and divisions, many leading firms are actively experimenting and laying the foundations to operationalize and harness its capabilities at scale.
While we are still in the nascent stages of generative AI, three key conditions — technology advancement, customer adoption, and regulatory clarity — indicate that the technology is here to stay. The major tech players are constantly vying for market share, investing heavily in research and development, and rapidly releasing new and improved models and features.
Customer interest and adoption of generative AI have also continued to grow quickly. In Oliver Wyman Forum’s recent Global Consumer Sentiment survey, 62% of respondents reported engaging with generative AI within the past three months. Despite concerns surrounding the potential risks, an encouraging 49% of participants expressed a high level of trust in the technology.
Although regulation typically lags behind the innovation curve, there have been significant regulatory developments in the AI landscape. The G7 recently reached a consensus on an AI Code of Conduct, while President Biden signed an executive order mandating a comprehensive framework for safe, ethical, and secure AI advancement. Shortly thereafter, the Bletchley Declaration on AI was announced, with leaders from 28 countries and the EU pledging to establish a global framework for AI safety. Notably, high-risk sectors such as financial services will be required to meet specific technical standards, which will be drafted in the coming years.
How generative AI could transform banking
The potential of generative AI seems boundless, but the truly transformative applications in banking are still unknown — but two plausible versions emerge.
On the more radical end of the spectrum, we foresee a potential future scenario where banking value chains undergo a dramatic overhaul. AI-powered agents would assume a central and proactive role in decisions such as authorization of payments, while investigative processes would become fully automated or replaced.
The more probable scenario, however, is an evolution rather than a revolution. Banking value chains would remain familiar, but banks would channel their energies into applications, crafting a multitude of innovative generative AI use cases to streamline the user experience for both consumers and employees. In this vision, generative AI would become an integral, largely unnoticed component of daily life where users, recognizing the substantial advantages the technology brings, are “trained” to tolerate its limitations.
Where banks now stand in generative AI
Most banks are still in the early stage of deploying generative AI and have yet to make significant progress in its adoption. Still, we have seen leading players proactively exploring opportunities. Their immediate-term focus has been on developing lower-risk, internal-facing applications that demonstrate tangible productivity benefits in day-to-day operations.
Early examples of this include generative AI-powered “co-pilot” tools designed to augment employee decision-making processes, particularly within the back- and middle-office functions. Thus far, the preference has been for off-the-shelf solutions with simple contextualization and customization options. These solutions are scalable and cater to various departments, ranging from IT for code generation to the legal and compliance teams for policy and regulation summarization and report drafting.
Companies already have realized significant benefits. For instance, Marsh McLennan deployed a proprietary generative AI assistant called LenAI, and early adopters reported saving an average of eight hours per week when using the tool. Additionally, they reported spending 20% less time on simple, repetitive tasks, and reallocating that time toward more complex tasks. This, however, merely scratches the surface of the true impact generative AI can achieve. We anticipate mass uptake in key functions in the coming years — both ready-to-use co-pilot tools and more tailored productivity boosters for specific banking functions.
Integrating generative AI with other AI technologies
Leading banks also are looking beyond individual use cases and focusing on big wins that could transform their operating models. We can anticipate the emergence of increasingly sophisticated applications that harness the power of both generative AI and conventional predictive AI/machine learning technologies. This integration will unlock a new realm of capabilities and further propel the potential of AI within the financial industry.
Through Oliver Wyman’s work, we have already witnessed some impactful pilot applications. For instance, one prominent global bank is leveraging a combination of AI technologies to empower its risk and compliance managers. In the face of a rapidly evolving regulatory landscape and a vast amount of information from various data sources, this innovative solution automatically keeps these managers informed about regulatory updates and changes in real-time. Moreover, it extracts key insights in a manner that aligns with the expertise and decision-making processes of a typical compliance officer. In another client example, a bank deployed a surveillance tool that combines generative AI and reinforcement learning. The tool monitors employees’ social media marketing activities, flagging any suspicious or unauthorized behavior that may result in regulatory censures.
Actions banks must take immediately to adopt generative AI
To secure a competitive edge in the evolving landscape, banks are at a crucial juncture for determining where and how to begin. Here are four actionable steps for banks today:
Set priorities based on learning and experiments
With the surge in interest in generative AI, banks often find themselves inundated with potential applications. Traditional prioritization methods, conducted without a practical grasp of the technology, can lead to misguided decisions. To identify truly impactful opportunities, banks must build a clear understanding of the capabilities and limitations of generative AI. They must also adopt a practical, hands-on approach to rapidly test feasibility through actual use case experiments, enabling them to assess risk and validate business cases. This approach ensures that investments are placed strategically.
Strike the right balance between risk and governance
Inaction is not the low-risk option when it comes to generative AI. Banks must establish a robust risk and control framework, setting guardrails firmly grounded in a comprehensive understanding of AI risks and their potential consequences. However, existing risk management processes need an overhaul to effectively manage risks emerging from generative AI. These processes often focus on traditional model risk metrics such as minimizing error rates, an approach that may be myopic. It is essential that organizations recognize the long-term impact of generative AI on their standing and prioritize the management of reputational and legal risks.
Accelerate technology readiness through a multi-speed approach
To expedite their journey into generative AI, banks must adopt a more balanced, multi-speed approach, facilitating safe and fast experimentation for key teams tasked with building prioritized use cases in the immediate term. Simultaneously, they must ensure that their technology infrastructure and proprietary data is ready for the longer term to power future modelling requirements with the required flexibility, while also prioritizing aspects of security and privacy.
Upskill talent to harness productivity gains
The demand for generative AI skills is expected to outpace the available talent pool. To bridge this skills gap, organizations must proactively institute upskilling and reskilling initiatives.
The skills leaders in generative AI need
While the immediate actions described above set the foundation, true leadership in generative AI extends beyond these measures. Leaders need to have a longer-term view by preparing for future disruptive scenarios that the technology could bring. That requires establishing the right organizational environment for innovation.
First, effective leaders articulate a clear vision for AI adoption aligned with their internal risk tolerance and organizational objectives. This strategic clarity becomes a guiding force in helping banks decide their big bets for future growth. Second, these leaders possess the ability to discern emerging trends, both within and beyond the banking industry. They excel in identifying and validating novel business models and product value propositions deeply rooted in the bank's core strengths. Finally, a hallmark of generative AI leadership is the ability to swiftly adapt global innovations to fit local market dynamics. Leaders excel not only in innovation but also in the strategic acquisition of emerging technologies, ensuring relevance in a rapidly changing landscape.
Generative AI has surged in the past year, with tech giants driving innovation and regulators emphasizing safe, ethical, and secure development. The future for banks in generative AI promises transformative potential, necessitating immediate actions. Organizations must identify impactful use cases, balance risk and governance, accelerate technology readiness, and rapidly upskill talent. True leadership requires foresight, trend discernment, and the agility to adapt innovations locally. As generative AI cements its place, organizations need to act swiftly to position themselves at the forefront of this revolution.