Just over a year ago, large language models like ChatGPT burst onto the scene, opening the door to both unprecedented AI capabilities but also new challenges. For banking executives, several questions needed to be addressed. Where can generative AI alleviate structural weaknesses and boost productivity? How can it elevate customer experiences and increase engagement? How can generative AI impact innovation and what risks accompany rapid adoption?
The 2024 Global Outlook for Banking and Financial Markets, published by the IBM Institute for Business Value, explores the impacts of generative AI based on in-depth analysis of financial reports, a survey of 600 banking executives, and insights from within IBM. The result is a comprehensive report on the future impact of generative AI in banking along with strategies for success.
The research examines generative AI’s potential to completely redefine banking, from revolutionizing relationships through ultra-personalized conversations, to streamlining operations, to reshaping workforce experience and accelerating the innovation process in financial services. The research also provides guidance on the need to balance risk and value enhancement, and how upgrades are needed to successfully integrate generative AI solutions. The IBM research also makes it clear that hesitation in embracing the potential of generative AI is not a path to success.
From the Banking Transformed Podcast:
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Seismic Change Offers Opportunities
An uncertain economy continues to challenge legacy business models in banking, with rising interest rates, deteriorating credit conditions, increased competition, and demand for new skills adding additional storm clouds on the horizon. Yet, recent technological changes offer new hope. There is no doubt that the meteoric rise of generative AI has propelled AI to the top of boardroom agendas in banking and beyond. For bank executives, key questions have emerged around where to start, how to deploy and how to balance risk and reward.
As consumers flock to nimble competitors, legacy banks must rethink business models and retool for the AI age, not only to boost efficiency but to radically reimagine both customer and employee experiences. In November of 2022, an exciting opportunity arrived in the form of large language models (LLMs) like ChatGPT that possess the ability to generate written content, images, code, and more.
It is clear that early hype has outpaced reality, but the IBM research reveals that LLMs are no passing fad. According to IBM, 78% of banks worldwide are already piloting or implementing tactical generative AI applications ranging from risk management to client services. Despite this enthusiasm, generative AI constitutes no silver bullet. Realizing its full potential requires data management, transparency, expanded privacy and compliance considerations that have proven to be challenging for many financial institutions.
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Transforming Client Relationships with Generative AI
In an accelerating trend, consumers continue to move away from branches toward digital channels at the same time they diversify their loyalty by testing non-traditional banking alternatives. According to IBM, 16% of consumers globally are already comfortable with a branchless, fully digital banking relationship — a figure set to grow exponentially. Meanwhile, mobile apps constitute the preferred platform across banking activities, from accessing accounts to executing transactions.
With consumer digital banking expectations in flux, generative AI offers new potential to defend against client attrition by providing conversational interfaces that feel more responsive and “human”. Early adopters globally are currently leveraging AI assistants to handle customer inquiries with enhanced perceptiveness and accessibility.
“Generative AI elevates decade-long investments to power mobile banking with cloud, ML and NLP. It can significantly improves client experiences with engagement based on personalized communication.”
— Paolo Sironi, IBM
Generative AI applications already in place aim not to fully replace, but to augment, human capabilities in executing sophisticated financial discussions. For instance, one European private bank utilizes generative AI to transform investment research into personalized client podcasts — optimizing relevance and timeliness. Elsewhere, AI is helping Charles Schwab construct tailored portfolios aligning to investor risk profiles and goals.
As client advisory represents a key component of value in the future, incumbent institutions must increasingly reorient branches from transactions to advice. In this scenario, generative AI can help initiate more meaningful, personalized guidance at scale across channels. The result could be improved loyalty even as clients migrate to digital channels for transactions.
Just as importantly, resolving friction points in digital customer journeys provides a strong use case for generative AI. To this end, AI techniques like search engine optimization uncover pain points during mobile interactions, directing corrective actions — anything from interface redesign to expanded digital support.
While there is much talk about how generative AI can improve customer experiences, banks are only beginning to tap AI’s potential in bridging digital experience gaps. One global payments firm cut complaint analysis time from three weeks to 15 minutes using generative AI, vastly improving responsiveness. Meanwhile AI has also helped US neobank Chime anticipate account overdrafts, automatically advancing deposits for coverage. This ‘financial GPS’ predictive capability can dramatically improve loyalty.
Most financial institutions understand that customer experiences and increased engagement remain key battlegrounds for relationships, where generative AI can drive differentiation beyond efficiency objectives. Prioritizing investments in this area will often generate quick and meaningful returns amid digital turbulence.
Reimagining Workforce Productivity
According to IBM, there is also a pressing need to transform operations for the AI age, replacing legacy inefficiencies with responsive, cloud-based architectures. These changes will indirectly improve the customer experience while enhancing workforce productivity through automation, and augmenting the workforce to elevate employee output.
“AI will not replace employees, but bankers capable to use AI will advance compared to those who don’t.”
Generative AI brings immense potential to workforce enhancement through its ability to analyze complexity (generating code, documentation, reports, or other business content). In many organizations, AI coding tools are already expediting the creation, testing, and deployment of new applications at scale. Beyond accelerating speed-to-market, automated code generation can lessen tedious tasks, freeing developers to focus on higher-value work.
Unfortunately, internal process transformation often remains too incremental and often viewed via the lens of legacy processes, instead of completely rethinking the back-office for the digital age. There is an urgent need for enterprise-wide efficiency boosts and workforce augmentation. Success requires strong change management and skill building so staff play to strengths alongside “digital coworkers”.
Accelerating Innovation and Increasing Value
The IBM report discusses the immense potential of generative AI to accelerate innovation and increase value exchange in banking and financial services in several key ways:
- Faster product development. Generative AI can significantly expedite software development by automatically generating code, documentation, and even full applications. This allows banks to bring new digital products and services to market much faster to meet changing customer needs.
- Personalized customer experiences. Banks can leverage generative AI’s natural language capabilities to create ultra-personalized conversations and interactions across channels. This builds deeper connections and relevance with customers.
- Streamlined operations. Generative AI excels at simplifying complex processes, whether by analyzing intricate systems to optimize them or by automating repetitive tasks. This improves back-office efficiency and productivity.
- Enhanced risk management. By scanning transactions, data, and codebases, generative AI bolsters fraud detection, predicts credit risks, exposes cyber vulnerabilities, and more. This reduces threats and ensures continuity of service.
- New revenue opportunities. With accelerated development speed and a deeper understanding of customer needs, banks can rapidly prototype and test innovative value propositions to fintech-style disrupt the industry before competitors.
“Human communication is the primary medium for banks to exchange value with final clients. Generative AI allows bankers to level up communication frameworks across their network to share more value with clients at lower costs.”
— Paolo Sironi, IBM
However, managing elevated data dependence, interpretability, and explainability risks remains imperative for generating sustainable value, trust, and adoption. Responsible governance and human oversight help steer innovation to its highest uses. Overall though, generative AI constitutes the catalyst banks need to transform legacy constraints into new strategic advantages.
The Future of Generative AI in Banking
Structural flaws still shackle most incumbent banks, reflected in weak cost-income ratios and low shareholder valuations. Meanwhile asset quality deterioration looms as rapid rate tightening threatens recession.
Want to go deep on AI best practices for banks?
Attend our AI Masterclass — Unlocking the Power of Artificial Intelligence in Banking — at The Financial Brand Forum 2024 on May 20-22 in Las Vegas. Led by Ron Shevlin, chief research officer at Cornerstone, this three-hour workshop will be jam-packed with lessons learned from industry leaders and real-world case studies.
For more information and to register, check out the Forum website.
Yet, monumental computational power now sits at executives’ fingertips should they seize it. Early movers are beginning to reap rewards in efficiency, risk management and client relevance. The next wave of generative AI applications will certainly define the winners in advanced data analytics, insights and deployed solutions,
However, ad-hoc deployments breed new dangers. Success requires addressing augmented workforces, resilient data architectures, transparent model governance and beyond. It is essential to find third-party solution providers that can provide the expertise needed to guide transformation at speed and scale.
With Silicon Valley threatening disintermediation in the financial services industy, revolutionary generative AI buys banks precious time to redefine operations and engagement models for the digital age. Executives face a seminal choice: fundamentally transform, or slide further into obscurity. For the vast majority, generative AI constitutes not just an option but an imperative.