At CFTE’s UK–Singapore Exchange during UK FinTech Week, Kenneth Gay, Chief Fintech Officer at the Monetary Authority of Singapore (MAS), addressed a gap that many financial institutions still face:
AI ambition is high. But practical adoption remains unclear.
While AI is widely discussed, many institutions are still struggling with fundamental questions:
- What solutions actually exist?
- Which ones are already deployed successfully?
- How do you assess them?
- How do you implement them safely?
As Kenneth highlighted, this is not a technology problem.
It is a navigation problem.
From Fragmentation to Shared Learning
One of MAS’s key responses to this challenge is the Pathfinder programme.
Rather than leaving institutions to explore AI in isolation, Pathfinder provides:
- Industry-level AI use cases
- Curated solutions across domains
- Shared best practices from institutions further ahead
The objective is simple:
move from fragmented experimentation to structured, collective learning.
This is critical because, as Kenneth noted during the session, many institutions are still duplicating efforts — solving the same problems independently instead of building on shared progress.
The Four Pillars of AI Readiness
Kenneth outlined a broader framework underpinning Singapore’s approach to AI in financial services:
- Adoption — helping institutions discover and implement real use cases
- Capability — building AI competency through dedicated centres and expertise
- Governance — developing practical, industry-led frameworks for risk management
- Talent — enabling upskilling, reskilling, and job transformation
All supported by a fifth dimension: international collaboration.
This reflects a key shift:
AI readiness is no longer about isolated initiatives — it is about building ecosystem-wide capability.
The Real Challenge: Knowing Where to Begin
Perhaps the most important insight from Kenneth’s session was this:
Many institutions are not blocked by technology — they are blocked by uncertainty.
Without visibility into:
- what works
- what is safe
- what is scalable
AI adoption slows down significantly.
The Takeaway
AI readiness is not just about building models.
It is about building an environment where institutions can:
- learn faster
- adopt responsibly
- scale collectively
Singapore’s approach shows that the future of AI in finance will not be built by individual firms alone —
but by ecosystems that enable shared progress.
