Will Generative AI Fundamentally Reshape Banking?

Will Generative AI Fundamentally Reshape Banking?

by December 11, 2023

A technology revolution is underway that stands to transform the banking industry fundamentally. Generative AI, which burst onto the scene in early 2023, leverages advanced natural language models to automate a vast range of cognitive tasks. As this versatile innovation proliferates across industries, banking leaders are moving swiftly to harness its potential.

 Two-thirds of senior digital and analytics leaders surveyed at a recent McKinsey forum on generative AI said they expect the technology to reshape their business profoundly. 

The pressing challenge they now face is not whether but precisely how and where to implement generative AI to maximise value creation for their institutions.

The economic impact of Generative AI in banking

The McKinsey Global Institute estimates that across various industries worldwide, generative AI could contribute an annual value ranging from US$2.6 trillion to US$4.4 trillion. Banking, in particular, stands to gain significantly, with an estimated yearly potential of US$200 billion to US$340 billion, equivalent to 9 to 15 percent of operating profits.

Will Generative AI Fundamentally Reshape Banking?

Significantly, while much-existing focus is trained on the massive productivity benefits generative AI enables through task automation, its influence promises to be far more multifaceted. 

The technology harbours the potential to fundamentally transform operating models, customer interfaces, and business partnerships, giving rise to novel banking business models altogether.

Senior bank executives face complex considerations in plotting their generative AI strategy. How extensively will generative AI reshape their value chain? Which new opportunities might it reveal that necessitate adjustments to strategic direction? What partnerships or capabilities will be imperative to cultivate in advance? 

While smartphones took years to steer banking operations firmly into the mobile age, the adoption of generative AI is progressing at warp speed by comparison. 

Consider Goldman Sachs – its developers are already implementing an AI tool to systematise labour-intensive testing procedures that were previously manual. Meanwhile, Citigroup employs generative AI to model the impact of pending US capital rules. 

For institutions too sluggish to pivot in response, such abrupt change could severely stress brittle operating structures unaccustomed to technological flux.

Challenges in scaling Generative AI

Scaling up generative AI within the banking industry presents a unique challenge, distinguishing it from traditional technology adoption. These challenges arise due to several key factors. First, the scope and implications of generative AI introduce advanced analytics capabilities and applications. 

This demands management teams to navigate unfamiliar terminology and potential pathways, requiring strategic positioning to seize the various opportunities that generative AI can create. Another challenge is the coordination complexity. 

Integrating generative AI adds complexity to the dynamics between business and technology in financial institutions. Analytics and data have gained prominence, necessitating deeper collaboration between business and analytics teams, often with differing priorities. Additionally, the rapid pace of change is a significant factor. 

Unlike the gradual transition to digital banking, generative AI is being accelerated, compelling banks to adapt swiftly to avoid stress on their existing operating models. Lastly, talent challenges are notable. Banks lacking in-house AI expertise face the formidable task of enhancing their capabilities through training and recruitment.

Successful scaling of Generative AI

Successfully scaling generative AI in the banking sector requires a strategic approach focusing on seven critical dimensions. It begins with a strategic roadmap, where banks commence their journey with a strategic outlook. 

Understanding where generative AI can substantially impact businesses is crucial. It is essential to secure alignment from senior leadership, pinpoint priority domains, set clear objectives, evaluate the necessary capabilities, and develop a comprehensive scaling-up plan.

Talent forms another critical aspect. Investing in executive education to deepen the understanding of generative AI among leadership teams is vital. It’s important to emphasise the technology’s connection to the bank’s operations, address employee concerns related to automation, and commit to an ongoing approach to upskilling.

In terms of operating models, encouraging cross-functional collaboration is vital. This approach facilitates the seamless implementation of generative AI, enabling product teams to work in close conjunction with business units and modify processes to meet the requirements of speed, scale, and adaptability.

When considering technology, strategically deciding whether to build, purchase, or establish partnerships for generative AI solutions becomes a focal point.

Thoughtful consideration of the architectural components is needed to ensure seamless integration with existing systems and workflows. The significance of data, especially unstructured data, in generative AI applications cannot be understated. 

It is necessary to develop capabilities to harness its potential effectively, emphasising data quality and considering security implications. Risk and controls also play a crucial role.

Addressing the novel risks associated with generative AI, including challenges related to model interpretability and unbiased decision-making, requires a comprehensive overhaul of risk and model-governance frameworks. 

Finally, focusing on user adoption and change management is crucial for successful generative AI scaling in banks. This involves creating user-friendly AI solutions, a solid change management strategy that engages everyone provides training, sets an excellent example through leadership, and offers clear incentives.

The scale of the opportunity

Generative AI’s potential to transform banking operations is simply enormous in magnitude. From streamlining client onboarding to detecting financial crimes to tailoring advice, the practical applications already number to the dozens, with many more still being uncovered. 

Yet successfully harnessing this promise at scale remains a complex challenge with many organisational dimensions. Banks able to skillfully activate the essential enablers from strategic vision down to user-centric design stand to solidify significant first-mover advantage. 

For those slower to embrace generative AI’s generational opportunity, the playing field of the future may leave them struggling to catch up.