Banking in the AI Era: 3 Ways Generative Technology is Redefining the Industry

One year following the public launch of ChatGPT, generative AI remains a dominant topic in the business world, and for good reason. Research indicates that generative AI has the potential to contribute nearly $7 trillion to the global GDP.

But what exactly does generative AI have in store for the banking sector?

Currently, much of the discourse surrounding this technology generates more excitement than clarity. During our “Meet the Experts” session at Sibos 2023, titled “How Generative AI is Set to Transform the Banking Industry,” we presented our latest research and analysis. We aimed to provide concrete data to steer the conversation away from excessive hype and speculation.

Here are the key insights we shared during our presentation

Generative AI, often referred to as “gen AI,” is poised to bring about transformative changes in various industries, with banking being no exception. While the core functions of banking, such as deposit-taking, lending, and payment management, will remain constant, the way these functions are performed is set to undergo a profound transformation thanks to Gen AI.

In essence, Gen AI will revolutionize the processes and methodologies employed within the banking sector. It’s widely acknowledged that Gen AI holds significant importance in the banking industry. However, there is a degree of caution regarding its implementation and application. Banks that hesitate to embrace this technology risk falling behind their more forward-thinking counterparts.

Early adopters are already witnessing substantial efficiency improvements. For instance, one global bank has successfully reduced email traffic by 40% in its first year by implementing an email routing system powered by AI and machine learning. Coding tools like GitHub’s generative AI Copilot have enabled bank programmers to write code up to 55% faster, with the AI even generating 46% of the code itself. Additionally, some financial institutions, such as Morgan Stanley, have deployed Gen AI tools to enhance their financial advisors’ access to a vast repository of reports and documents during client interactions.

These instances are just the tip of the iceberg. Our research, based on data from authoritative sources, indicates that Gen AI has the potential to revolutionize a significant portion of work across various industries. In banking, this potential transformation is particularly pronounced, with 73% of banking-related tasks having the potential to be influenced by new technologies, including large language models like ChatGPT. More specifically, 39% of banking tasks could be automated without requiring interpersonal or cognitive skills, while 34% could be augmented by incorporating interpersonal communication, proactive reasoning, or expert validation.

As pioneers in adopting these advancements leverage their newfound efficiencies and extend superior customer experiences on a larger scale, generative AI technology’s transformative influence will permeate the banking industry.

This heightened impact underscores the imperative for banks and other users of generative AI to establish appropriate safeguards around these innovative tools. The full potential of generative AI in banking can only be realized through responsible and secure usage in alignment with regulatory requirements.

We can delineate this wave of change instigated by generative AI across three primary areas of influence. Generative AI empowers banks to:

• Foster Innovation and Distinguish Themselves

Generative AI introduces many opportunities for banks to enhance their revenue through data-driven decision-making and improved customer experiences. An illustrative instance is the earlier-mentioned case at Morgan Stanley.

Another noteworthy example involves a major North American bank employing generative AI to scrutinize customer data and financial histories to assess credit exposure. This facilitates more informed lending decisions and mitigates the risk of loan defaults.

• Revolutionize Mid- and Back-Office Operations

 

Generative AI is poised to play a substantial role in streamlining the management of mid- and back-office operations within banks. This entails cost reduction, akin to the email routing system example mentioned earlier, and the liberation of intellectual capital for innovation by automating repetitive tasks such as reporting, data entry, and transaction processing. As a result, bank staff will have more time for creative endeavors and personalized customer service.

A tangible illustration of this shift can be seen with a European bank employing a large language model (LLM) to support customer service representatives during customer calls. The LLM automates note-taking for the representative and retrieves pertinent information, allowing the human agent to focus on assisting the customer.

The extent of this impact will naturally vary across different roles within the organization. Our research indicates that 37% of the time spent by customer service representatives in banking could be automated by generative AI (as exemplified by the note-taking scenario). In comparison, 28% could be augmented by surfacing relevant information during customer interactions.

• Integrate Generative AI into Operations and Tools for Enhanced Productivity

The ecosystem of software partners integral to the banking industry is progressively incorporating generative AI into all facets of their offerings. This integration promises to boost productivity for bank employees across various functions significantly.  For instance, Microsoft initiated the integration of LLMs into its Microsoft 365 suite of applications in March 2023 with the launch of Copilot. Adobe’s Firefly tool can generate images based on simple text prompts. Salesforce offers a generative AI-powered CRM assistant called Einstein, while Workday has recently announced plans to integrate generative AI into its suite of tools.

These offerings represent initial forays into the field, with vendors poised to iterate, enhance, and compete in this space over time. For banks, the crucial decision revolves around determining which generative AI features warrant the additional investment.

The Transformative Power of Generative AI on Growth

Our analysis further reveals that within three years, generative AI has the potential to act as a catalyst for exponential growth in a bank’s operating income, outpacing existing consensus forecasts. A combination of increased revenue and reduced costs drives this growth.

On the revenue front, the majority of the benefits stem from generative AI’s impact on client-facing activities. It could lead to a 17% increase in the time dedicated to client interactions and advisory services, collectively contributing to approximately 80% of banking revenue. This additional time investment could result in a noteworthy 9% uptick in revenue.

In terms of cost reduction, we project a significant 9% to 12% decrease in mid- and back-office costs, realized through a productivity boost of 7% to 10% in corporate functions.

By synergizing the effects of generative AI across revenue and cost management, we arrive at the remarkable conclusion that generative AI can amplify a bank’s operating income by a substantial 25% to 40%. This projection significantly surpasses the existing growth forecasts for banks until 2026, doubling or even tripling their expected performance.

In light of such remarkable growth prospects, banks must pay attention to the transformative potential of generative AI.

For those unsure of where to initiate their generative AI journey within the bank, our foremost recommendation is to promptly establish a dedicated generative AI SWAT team. This team should comprise leaders from the bank’s business and technology facets and focus on strategy, policy, talent acquisition, technology implementation, and data management.