Singapore and UK Forge AI-in-Finance Partnership: What It Means for Your Firm
The Monetary Authority of Singapore (MAS) and the UK Financial Conduct Authority (FCA) have announced a new strategic partnership on AI in finance. The goal is clear: support safe, responsible innovation and help firms scale best-in-class AI solutions across both markets.
For banks, asset managers, and insurers, this means faster experimentation, clearer expectations, and more consistent guardrails across two major financial centers. It also opens up cross-border learning and collaboration with regulators directly involved.
What the partnership includes
- Joint testing of AI solutions across Singapore and the UK.
- Exchange of regulatory insights and ongoing discussions on responsible AI.
- Collaborative events to spotlight best-in-class approaches for production use.
- Expansion of industry programs - MAS PathFin.ai and FCA AI Spotlight - to cross-share quality solutions and strengthen cooperation.
Learn more about the regulators driving this effort: Monetary Authority of Singapore and the Financial Conduct Authority.
Signals from the regulators
"AI is redefining the future of finance - moving from experiments to enterprise use, and from individual models to connected, agentic systems," said Kenneth Gay, Chief FinTech Officer at MAS. "As this shift accelerates, MAS' priority is to ensure that adoption is both safe and scalable."
Jessica Rusu, Chief Data, Information and Intelligence Officer at the FCA, said the partnership with MAS will raise global influence in a strategically competitive space. "UK and Singapore firms will be able to grow through collaboration, gauge new cross-border opportunities, and shape the future of responsible AI innovation in finance."
Why this matters for finance leaders
Cross-border AI deployment should get smoother, with more aligned expectations on testing, oversight, and outcomes. That means lower friction in moving from pilot to production across both markets.
Risk and compliance teams can expect clearer pathways to validate explainability, fairness, and controls. Product and data leaders get a channel to test real use cases with regulator insight early, not after launch.
What to do next
- Create a single inventory of AI models and map each to a business outcome, owner, data sources, and critical risks.
- Assemble a "ready-to-test" pack: data lineage, model cards, validation results, bias and performance metrics, human-in-the-loop controls, monitoring thresholds, and incident playbooks.
- Plan for cross-border operations: data residency, PII handling, evaluation criteria parity, and MLOps portability between jurisdictions.
- Tighten vendor diligence for third-party AI: security posture, training data provenance, evaluation reports, service-levels for model drift and retraining.
- Upskill teams on practical AI-in-finance workflows and controls. If you need a curated starting point, see this resource: AI tools for finance.
What to watch next
- Announcements on joint testing windows, tech sprints, and showcase events.
- Guidance updates on responsible AI expectations and evaluation standards.
- Interoperability between assessment frameworks and sandboxes across both regulators.
- Early case studies from financial institutions deploying AI under this partnership.
The bottom line: this partnership gives your teams a clearer route to scale trustworthy AI across Singapore and the UK, with direct input from the regulators who matter. If you prepare your models, controls, and documentation now, you'll be ready to move first when the testing doors open.
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