Your AI Can’t Run Itself. Neither Can Your Quant Team.

The models are faster. The data is bigger. The compute is cheaper. The only thing financial services can't seem to scale is the humans smart enough to run it all. A new industry survey makes that concrete. And for AI and data leaders at Singapore’s banks, asset managers, and insurers, it reads more like a warning order than a report.

The CQF Institute, part of Fitch Learning and the awarding body for the Certificate in Quantitative Finance (CQF), recently published its 2026 Careers in Quantitative Finance Survey, drawing on responses from 135 quant practitioners worldwide. 88% say a skills gap exists across the industry. 76% say it has grown in recent years. That’s a crisis underway.

The driver is AI — not as a vague disruption force, but as a concrete daily demand on headcount. 58% of respondents say AI has expanded the scope of their responsibilities over the past 2 years, pulling them deeper into machine learning, statistical modeling, and advanced programming. 74% expect AI to drive a major or complete transformation of quant roles within five years. The job is already different. And it will get more different faster than most hiring pipelines can absorb.

“Financial institutions are discovering that deploying advanced AI systems requires far more professionals with strong quantitative and computational foundations than the market currently produces,” said Dr. Randeep Gug, the managing director of CQF Institute, in a press release.

For Singapore’s FSI leaders, the stakes are sharper still. The Monetary Authority of Singapore has staked out an explicit ambition: to make Singapore a global AI hub, with financial services leading the charge. Banks are deploying AI across risk models, credit decisioning, fraud detection, and regulatory stress testing. But the professionals needed to build, validate, and govern those systems are in short supply, and getting shorter.

The survey’s most sobering number isn’t about hiring. It’s about what’s already inside the building. 75% of current practitioners say their roles require capabilities they were never taught at university. If your existing quant team is running skills deficits, your AI systems may be less well-governed than your board believes. Earlier CQF research from 2025 reinforces the upstream supply problem: fewer than 9% of quant finance professionals think new graduates arrive equipped with the AI and machine learning skills the industry actually needs.

And then there’s the risk hiding in plain sight. 39% of respondents identify the greatest danger of the skills gap as increased reliance on automated systems without sufficient human oversight. Banks are building AI-driven infrastructure at pace. The people qualified to oversee it are scarce. That’s not theoretical model risk. That’s live operational exposure, accumulating quietly, every day a position goes unfilled.

“As AI continues to reshape roles and expand job requirements, continuous reskilling is essential to keep pace with the industry's demands,” said Dr. Gug.

84% of survey respondents agree: continuous reskilling is vital for a successful quant career. For CDAOs and heads of quantitative risk in Singapore, that translates into a governance imperative. A talent audit should accompany every AI deployment decision. Do you have the people to validate what the model is doing? To catch what it gets wrong? If not, you have a governance problem wearing a technology costume.

Image credit: iStockphoto/Pornpimone Audkamkong