Bank CFOs weigh broader returns from AI investment

Financial institutions are prioritising productivity and risk before using AI to pursue customer growth and faster product development.

Bank chief financial officers should look beyond immediate financial returns when deciding whether artificial intelligence investments are worth expanding, as early use cases may deliver benefits that are difficult to quantify.

Ronald Wong, Leader for Financial Accounting Advisory Services in ASEAN and Singapore at EY, said scaling AI requires a longer-term view rather than treating implementation as a standalone technology investment.

Banks are initially concentrating on productivity and risk, which Wong described as a logical sequence rather than evidence that institutions are overlooking growth.

Productivity represents a relatively accessible starting point, whilst risk remains particularly important for regulated financial institutions. Once banks establish these foundations, they can turn their attention towards growth opportunities, including those facing customers.

Assessing returns presents another challenge for CFOs, who traditionally rely heavily on financial metrics when evaluating investments. Wong said AI remains relatively new, making the outcome of some projects uncertain. Banks can identify specific use cases, test them through pilots and expand those that demonstrate potential.

“You’ve got to also look at the qualitative factors of the benefits of AI,” he said, rather than expecting every dollar invested to immediately generate an equivalent financial return.

Those benefits can include stronger customer engagement and product development.

AI could help banks personalise product offerings, potentially improving the customers they attract, and reducing the workload involved in selling products.

Wong said the technology could also augment employees’ skills and experience during product development, allowing banks to bring improved products to market more quickly.

For CFOs, the investment case therefore extends beyond near-term cost savings. Banks must first establish productivity and risk controls, then assess whether successful AI applications can support customer growth, product innovation, and longer-term organisational change.