model is faster. The data is even bigger. Computation costs are lower. The only thing financial services can’t seem to scale is people who are smart enough to do it all. A new industry study makes that clear. And for AI and data leaders at Singapore’s banks, asset managers and insurance companies, this is more of a warning order than a report.
The CQF Institute, part of Fitch Learning and the awarding body of the Certificate in Quantitative Finance (CQF), recently released its 2026 Careers in Quantitative Finance Survey, based on responses from 135 quantitative practitioners around the world. 88% say a skills gap exists across their industry. 76% say they have grown in recent years. It’s an ongoing crisis.
AI is the driving force. Rather than a vague disruptive force, AI is a concrete, everyday demand on personnel. 58% of respondents said that in the past two years, AI has expanded their responsibilities and become more involved in machine learning, statistical modeling, and advanced programming. 74% expect AI to significantly or completely transform the role of quants within five years. My job is already different. And more changes will occur faster than most recruiting pipelines can absorb.
“Financial institutions are realizing that deploying advanced AI systems requires far more experts with strong quantitative and computational foundations than the market currently produces,” Dr. Randeep Gugu, managing director of the CQF Institute, said in a press release.
For Singapore’s FSI leaders, the risks are even starker. The Monetary Authority of Singapore has a clear ambition to make Singapore a global AI hub, with financial services at the forefront. Banks are deploying AI across risk models, credit decisions, fraud detection, and regulatory stress testing. However, the experts needed to build, validate, and manage these systems are in short supply and are becoming increasingly scarce.
The survey’s most alarming numbers aren’t about employment. It’s about what’s already in the building. 75% of current practitioners say their role requires competencies that were not taught at university. If existing quant teams suffer from skill shortages, AI systems may be less well managed than boards think. Initial CQF research from 2025 reinforces upstream supply issues. Less than 9% of quantitative finance professionals believe that new graduates enter the company with the AI and machine learning skills that the industry actually needs.
And risks are hidden in plain sight. 39% of respondents identified the biggest risk of the skills gap as increased reliance on automated systems without sufficient human oversight. Banks are rapidly building AI-driven infrastructure. There is a lack of qualified people to oversee it. That’s not a theoretical model risk. This is live operational exposure that accumulates silently as positions are left unfilled each day.
“As AI continues to reshape roles and expand job requirements, continued reskilling is essential to keep up with industry demands,” said Dr. Gug.
84% of survey respondents agree that continuous reskilling is essential to a successful career in quants. For CDAOs and Quantitative Risk Officers in Singapore, this is a governance imperative. Any decision to implement AI should be accompanied by a talent audit. Do you have people to verify the model’s behavior? How can I understand what’s going wrong? If not, there is a problem with governance in the guise of technology.
Image credit: iStockphoto/PornPimone Oakham Kong
