Singapore’s financial sector has launched an initiative to prepare more than 80,000 local financial services workers for artificial intelligence by 2028. The IBF AI Workforce Co-Lab brings together 23 financial institutions, trade unions, and education partners to introduce AI training and redesign existing jobs.
The initiative, launched on September 24, 2026, by Deputy Prime Minister and Monetary Authority of Singapore (MAS) Chairman Gan Kim Yong, focuses on helping employees apply AI in their daily work rather than simply learning how the technology operates.
For workers in banking, insurance, and wealth management, the program could change the skills employers expect and the responsibilities attached to existing roles.
How Far Will the AI Workforce Initiative Reach?
The 23 participating institutions represent approximately 40% of Singapore’s financial services workforce. Participants include DBS, OCBC, UOB, AIA Singapore, Prudential, Standard Chartered, and JPMorgan Chase.
According to the supplied research, more than half of the 80,000 targeted employees have already completed foundational AI programs recognized by the Institute of Banking and Finance (IBF).
DBS, for example, has equipped around 14,800 Singapore-based employees with basic AI capabilities.
The next phase moves beyond general AI awareness. Participating institutions are expected to connect training with actual job responsibilities, with the 2028 target covering all staff across their Singapore operations.
The target concerns training coverage. It does not establish that every employee will use the same AI tools or that all financial jobs will be transformed in the same way.
Three AI Training Pathways for Finance Professionals
The Co-Lab organizes its training around three groups of responsibilities.
| Pathway | Skills and responsibilities |
|---|---|
| Leadership | AI strategy, governance, and workforce transition |
| Wealth management | Generative AI, client engagement, and advisory expertise |
| Operations | Process improvement, exception handling, risk oversight, and AI validation |
The distinction is important because AI adoption affects different financial roles in different ways.
Managers need to understand how AI changes business processes and introduces new risks. Wealth managers may use AI to prepare research and advisory materials, while still relying on professional judgment and client relationships.
Operations employees need to identify errors, handle unusual cases, and check automated outputs before they are used.
The program’s approach combines technical skills with existing financial expertise instead of treating AI knowledge as a replacement for professional experience.
Where AI Is Saving Time in Banking
Financial institutions can use generative AI and data-processing systems to organize information, prepare documents, and consolidate research.
The supplied research brief includes two company-reported examples of time savings.
| Institution | Task | Reported time reduction |
|---|---|---|
| UOB | Report consolidation | 60 minutes to 30 minutes |
| Standard Chartered | Research preparation and compliance documentation | 1–2 days to 15–20 minutes |
These examples illustrate potential efficiency gains, but they are not independently verified industry-wide results.
At UOB, automated consolidation reduced compilation time while retaining mandatory manual validation. Standard Chartered reported shorter preparation times for client and market research tasks.
For employees, the potential benefit is less time spent assembling information and more time available for analysis, complex cases, and customer communication.
However, faster preparation does not automatically produce more accurate decisions. Financial institutions still need reliable data, appropriate review procedures, and controls over how AI-generated material is used.
Why Human Oversight Remains Essential
Financial services involve sensitive customer information and decisions that can affect people’s money. Generative AI can produce inaccurate information, omit important details, or reflect weaknesses in its underlying data.
The Co-Lab’s operations pathway includes AI output validation, exception handling, and risk oversight. These skills are particularly relevant when automated systems assist with financial documents or operational decisions.
The research brief does not identify the specific AI platforms or commercial models deployed across the participating institutions. Nor does it establish that every bank follows identical governance procedures.
Training employees to recognize AI risks is one part of responsible deployment. It does not, by itself, eliminate errors, data privacy risks, or potential bias.
Partnerships Aim to Redesign Existing Jobs
IBF has introduced a job redesign playbook for human resources leaders and a pilot workflow-mapping program with Ngee Ann Polytechnic.
The initiative also includes an enhanced agreement with the NTUC Financial and Professional Services Cluster of Unions and seven industry associations.
These partnerships connect training with changes in job responsibilities. For example, employees whose routine processing tasks become automated may need opportunities to develop skills in process optimization, risk management, or AI supervision.
Singapore’s Sustainable Finance Jobs Transformation Map, launched in 2024, provides an earlier example of sector-wide training. The supplied brief reports more than 21,000 training completions under that initiative.
Whether the new Co-Lab will lead to higher wages, additional jobs, or significant changes in total employment remains uncertain. The available research does not provide quantitative forecasts of net job creation or post-2028 headcount.
What Readers Should Know
Singapore’s AI Workforce Co-Lab combines foundational AI training with role-specific skills, job redesign, and human oversight. Its immediate objective is to prepare financial services employees for AI-related changes by 2028.
For workers, the initiative highlights the importance of combining AI literacy with critical thinking, professional judgment, communication, and the ability to review automated outputs.
The long-term effects on productivity, salaries, and career opportunities will depend on implementation and measurable results that have yet to be established.
