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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.

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.

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