AI in Banking: AI Will Be An Incremental Game Changer

Given banks' material investment capacity, management of large amounts of proprietary data, and often fluid business models, it was perhaps inevitable that they proved to be enthusiastic early adopters of machine and deep learning technology (so-called traditional AI). These systems have (for decades, in fact) been used to improve risk management processes, loss mitigation, fraud prevention, customer retention, and to deliver efficiency gains and profit growth.

For the same reasons, it is also little surprise that banks are now poised to take a further step in integrating more powerful 'generative AI' technology in their operations.

This new wave of AI promises to reshape the industry, at a steady and incremental rate, by providing new capabilities, revenue opportunities, and cost reductions. Over time, that could tilt the competitive landscape in favor of those banks that best utilize AI's potential. S&P Global Ratings believes that the changes AI will usher in could also have implications for our assessment of banks' credit quality.

Chart 1

AI In Banking Is Not New, But It Is Changing

To date, most AI use cases in banking have aimed to either automate tasks or generate predictions. This work has been done by supervised and unsupervised machine learning (ML) models (and sometimes more complex deep learning models) that require significant computing capacity, and large amounts of data. The application of machine learning in banking accelerated in the late 2000s with the development of Python for Data Analysis, or pandas--an open-source data analysis package written for the Python programming language. Pandas, along with other machine learning software libraries, like SKLearn and TensorFlow, made data structuring and analysis easier, more systematic, and thus opened the door to more accessible machine learning algorithms and powerful analytical frameworks. Financial analysis has also been a natural recipient of innovative, data-intensive applications, notably from other disciplines. Examples include life tables from insurance, Monte Carlo simulations and stochastics from physics, which, in turn, drove new developments in machine learning and related technologies. 

It is testament to the benefits of this earlier AI that (despite its complexities) banks, financial service providers, and the insurance sector emerged as some of its most active users. Machine learning in banking, financial services, and insurance accounted for about 18% of the total market, as measured by end-users, at end-2022 (see chart 2).

In their ML strategy, financial services companies seem to primarily rely on cloud-based machine learning services, such as AWS, Microsoft Azure, or Google ML (see chart 3). Furthermore, most (71%) still use private cloud environments, rather than the public cloud, according to a study by the TMT Research unit of S&P Global Market Intelligence, a division of S&P Global.

Chart 2

Chart 3

AI-led automation has principally facilitated banks' operational simplification and cost reduction, as banks have identified manual and mechanical tasks performed by staff and replaced them with computers that are not only cost effective but also less prone to operational failures. An example of AI in banking driving automation is Standard Chartered's document processing system, called Trade AI Engine, which was developed with IBM. It can review unstructured data in different formats, identify and classify documents, and learn from its own performance. 

Banks have also used AI capabilities and data, both proprietary and external, to augment employees' capabilities, enabling them to perform tasks that were previously beyond them. For example, prediction and recommendation models have leveraged AI's ability (primarily through unsupervised machine learning) to analyze vast amounts of data and uncover hidden patterns that wouldn't be apparent to a human. This has facilitated more accurate and faster decision-making. 

This pattern recognition has notably been employed in fraud detection and financial forecasting, where about 40% of financial services companies rely primarily on machine learning for both use cases, according to S&P Global Market Intelligence TMT.

Other examples of AI use cases in banking are set out below (see table 1).

Table 1

Source: Banks' websites and public announcements.

Generative AI Will Bring New Opportunities And Threats

While AI-facilitated automation and prediction are common parts of banks' digital transformation (at least for larger, and technologically sophisticated banks), investment in and adoption of tools driven by newer, generative-AI powered systems remain nascent (see chart 4). The possibilities (and risks) are thus yet to be fully tested. But the potential for the new AI to reshape banking seems vast. 

It is also worth recognizing that this new wave of AI will also deliver opportunities for the large and growing network of financial technology (fintech) companies. The resulting capabilities could magnify fintech's potential to disrupt the banking sector, and because of that increases pressure on banks to explore new applications for generative AI.

The powerful possibilities offered by Generative AI stem from its ability to create content based on the analysis of large amounts of data, including text, image, video, and code. That capability means it can, for example, be used to summarize content, answer questions in a chat format, and edit or draft new content in different formats. More specifically that means generative AI in banking could rapidly and cheaply (once the models are deployed at scale) generate hyper-personalized products and services, or accelerate software engineering, IT migration, and modernization of programs. It could also augment humans' abilities, through AI chatbots or virtual assistants--this is the focus of a partnership between Morgan Stanley and OpenAI, the U.S. research laboratory behind ChatGPT.

Chart 4

Generative AI In Banking: The Next Five Years

Global spending on artificial intelligence is expected to reach $166 billion in 2023 (with banking one of the largest contributors by industry at about 13%), rising to about $450 billion by 2027, according to a report by International Data Corp. (IDC), a provider of technology market intelligence and advisory services.

The ways in which generative AI will be used by banks is likely to hold some surprises, but it seems certain that the new technology will result in both an evolution and an expansion of AI's role within the banking sector.

Notable changes due to the application of generative AI in banking are unlikely to be immediate. We expect banks will continue testing generative AI models, and investing heavily in them, for the next two years to five years, before scaling up deployment to customers and engaging in more transformative projects. Furthermore, the bulk of banks’ near-term use cases will likely focus on offering incremental innovation (i.e., small efficiency gains and other improvements across business units) and will be based on specific business needs. Finally, we expect employees will remain in an oversight role, known as human-in-the-loop (HITL), to ensure results meet expectations (in terms of accuracy, precision, and compliance) as the technology matures.

Despite that gradual onset, the potential for wide-ranging application of generative AI means the banking sector is among those likely to experience the biggest impact from the advancement. On an annual basis, generative AI could add between $200 billion and $340 billion in value (9%-15% of banks’ operating profits) if the use cases are fully implemented, according to a 2023 report by McKinsey & Co, a management consultant.

Apart from new business use cases, banks are also likely to apply generative AI (through foundation models) to existing and older AI applications, with the aim of improving their efficiency. For instance, the digitalization and automation of customer-facing processes generates a digital data trail that generative AI can use to fine-tune both the service and its internal processes. This could then deliver further digitalization, including hyper-scale customization, that might enable better client segmentation and retention. Digital data trails could also be used to improve risk management, data collection, reporting, and monitoring. 

How banks go about developing their generative AI capabilities is likely to depend on their scale and investment capacity. Options range from outsourcing (via contracting to a third-party) to in-house development, and a wide range of hybrid solutions involving the fine-tuning of existing models. While most generative AI applications in banking remain at early stages of development, the spectrum of projects and approaches is already apparent (see table 2).

Table 2

Source: Banks' websites and public annoucements.

Benefits And Concerns Of AI In Banking

Generative AI's potential to benefit the banking sector, more than other sectors, comes from its ability to understand so-called natural language (language as it is commonly used). That is because natural language is often used in knowledge-based jobs characterized by higher education, effective communication, human collaboration, and logical-linguistic skills.  While the impact of generative AI on various bank operations will differ, the benefits could be significant. For example, improvements in the productivity of customer-service employees could deliver cost reductions and efficiency improvements. Chart 5 summarizes some of the potential benefits we expect to emerge with increased application of generative AI in banking.

Chart 5

As with any deployment of new technology, the adoption of generative AI will come with risks, costs, and concerns. That is particularly the case for the new iteration of AI given the pace at which the technology is evolving, its deployment capabilities across entire business functions, and the potential breadth of its use across industries and society. 

Those concerns include generic issues that are applicable to many industries, and others that are specific to banks. In that first basket are AI-related ethical concerns, such as the ability to explain generated content or biases embedded in data. Selection bias in banking, for instance, might perpetuate profiling issues based on gender, race, ethnicity, that could lead to unfair credit scoring and customer discrimination. Another key generic issue is environmental concerns (and criticism) due to the high levels of energy consumed by AI models--training a generative AI model consumes more energy per year than 100 American homes, according to estimates.

Issues could also arise as still-new AI regulatory frameworks mature, with the potential for differences to emerge in oversight and requirements across regions. That could affect many industries but will be particularly relevant for the banking sector, which is heavily regulated and faces higher conduct, reputational, and systemic risks than other sectors. As AI becomes increasingly regulated and new regulations extend across major geographies, banks could be exposed to the risk of fines or the regulatory suspension of some operations should models lead to poor customer outcomes (such as customer discrimination or data leaks), result in risk management failures that could have been avoided, or fail to meet transparency, safety, and robustness requirements.  

Other AI risks that are particular to the banking sector include issues revolving around security and privacy, risks related to workforce displacement by AI, and the risk of escalating AI investment required to keep pace with the digital transformation (see table 3).

Table 3