Bank CFOs evaluating artificial intelligence investments should look beyond immediate financial returns, as early use cases often deliver benefits that resist easy quantification. The advice comes from Ronald Wong, Leader for Financial Accounting Advisory Services in ASEAN and Singapore at EY, who argues that scaling AI requires a longer-term view rather than treating it as a standalone technology purchase.
The productivity-to-growth sequence
Banks are concentrating first on productivity and risk management, a logical starting point for regulated institutions. Wong described this as a deliberate sequence, not evidence that financial institutions are ignoring growth opportunities. Productivity improvements offer a relatively accessible entry point, while risk controls remain critical in a heavily supervised industry.
Once these foundations are in place, banks can shift focus toward customer-facing growth. "You've got to also look at the qualitative factors of the benefits of AI," Wong said, rather than expecting every dollar invested to immediately generate an equivalent financial return. Those qualitative factors include stronger customer engagement and faster product development cycles.
Measuring what traditional metrics miss
CFOs face a genuine challenge here. The profession relies heavily on financial metrics when evaluating investments, yet AI projects remain relatively new and their outcomes uncertain. Wong recommends a practical approach: identify specific use cases, test them through pilots, and expand those that demonstrate potential.
AI could help banks personalise product offerings, improving the customers they attract while reducing the workload involved in selling products. The technology could also augment employees' skills and experience during product development, allowing banks to bring improved products to market more quickly.
Building the investment case
For CFOs, the investment case 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. This requires evaluating both quantitative returns and harder-to-measure benefits like improved customer engagement.
Professionals looking to build expertise in this area can explore the AI Learning Path for CFOs, which covers financial strategy, forecasting, and risk analysis relevant to technology investment decisions. Broader training on AI for Finance also addresses applications in risk management and banking operations.
Why this matters for finance professionals
CFOs and senior finance leaders need to develop evaluation frameworks that capture both hard financial returns and qualitative gains from AI. Wong's guidance points to a staged approach: start with productivity and risk, prove value through pilots, then expand toward revenue-generating use cases. Pitching AI purely on cost savings may undersell its potential to reshape product development and customer relationships over time.
Your membership also unlocks:
