This is a thought leadership report issued by S&P Global. This report does not constitute a rating action, neither was it discussed by a rating committee.

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.

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).

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).

The banking sector is a regulated services industry that relies heavily on technology. Banks' ability to design and implement strategies that effectively capture AI's operational benefits could, like other new technologies (and potentially more so), have implications on our view of their credit quality (see chart 6).

AI strategies have the potential to provide competitive advantages to banks that have the capacity and flexibility to make best use of them. Well deployed AI could enhance operating revenues, by improving employees’ decision-making and by unlocking the revenue potential of clients--not least due to personalized services and products. And there could be a significant positive effect on costs, given the potential for a robust AI strategy in banking to simplify operations, reduce operating expenses, and thus improve efficiency and profitability. 

Shifts in these factors can be relevant to our assessment of banks’ business position, notably where they exacerbate the difference between banks’ competitive position by contributing to stronger franchises, and more agile and profitable business models.

Generative AI in banking promises to exacerbate these differences by also playing a role in banks' ability to upscale and modernize legacy IT systems--notably with low-code /no-code software that could offer important savings. This could also reduce operational risks and costs that arise from running banks on old infrastructure and labor-intensive systems. For example, we estimate that a 10% reduction in bank staff costs would, on average, improve return-on-equity by about 100 basis points and cost-to-income ratios by about 3 percentage points, based on S&P Global Ratings' Global Top 200 rated banks. These potential gains would have to be balanced against the investment in tech that they require, and against the opportunities for banks to reduce employee numbers (while maintaining revenues). Thus it remains to be seen to what extent banks that successfully deploy AI strategies materially outperform those that are AI laggards. 

AI also has the potential to enhance risk management and could thus influence our view of a bank’s risk profile, albeit indirectly. For example, in credit risk, a bank that can accurately price risk and use patterns hidden in data to determine the likelihood customers will repay debt (or become problematic) will improve its workout models, reduce problematic loans, and improve the accuracy of its provisioning. In effect, its risk-adjusted performance should improve, relative to peers’.

There are numerous ways that AI could be used to enhance risk management practices (see table 4). Yet, poor deployment of AI could equally lead to reputational and operational risks that could be detrimental to our view of a bank’s risk position.  

Not all banks, or indeed regions, will move at the same speed. Local competitive environments, regulatory developments, banks’ investment capacity, and customer preferences will all play a role in determining the extent to which regional AI usage proves to be conservative or more transformative. For example, we expect large scale and bespoke AI-driven client services to emerge first in countries where customers have more permissive attitudes to new technology, such as China, U.S.A., U.K., the Nordics, and Australia (see chart 7).

While generative AI is still in its infancy, it has the potential to become a general-purpose technology that is pervasive, enables complementary innovation, improves the quality of products and services, and reduces costs (similar to the internet revolution). Yet, in considering those potential benefits equal weight should be given to understanding the related risks and concerns (both known and yet to emerge). 

It thus remains to be seen at what speed, and to what extent, it makes business sense for banks to invest in transformational AI strategies.