AI in Financial Services: Emerging Global Norms

Drawing on developments across leading jurisdictions, the ‘AI in Financial Services: Emerging Global Norms’ report finds broad alignment on high-level principles from the OECD, G20 and G7 (human-centricity, transparency, robustness, safety and accountability). However, countries diverge in how they operationalise these principles from prescriptive regimes to principles-based and innovation-first approaches.

The IRSG’s analysis determines AI largely amplifies existing risk rather than introducing wholly new ones and advocates for interoperable, principles-based supervision anchored in existing technology-neutral rules and that no new hard global rules on AI should be created.  

Commenting on the report, Farmida Bi, Chair of the IRSG Council, said:

As AI transforms financial services, it is vital that regulatory approaches support both innovation and resilience. To realise its benefits safely, we need coherence without rigidity, shared taxonomies and supervision through existing rules. We don’t need new hard global rules for AI at present but, rather, international cooperation across regulators, policymakers, and international standard setters to share experiences on how AI is being deployed to identify where adjustments can be made to encourage interoperability of AI. This report provides clear, actionable steps that promote the safe and responsible innovation of AI in financial services.

Key findings include:

• Most jurisdictions draw on OECD/G20/G7 principles, yet implementation spans prescriptive rulebooks (e.g., EU) to non‑statutory, outcomes‑focused supervision (e.g., UK), and voluntary co‑created guidance (e.g., Singapore).
• AI is a general‑purpose technology that can magnify model risk, data governance, third‑party concentration, and cyber threats (particularly for generative AI) but does not introduce wholly new financial‑sector risks.
• Given AI’s rapid evolution, rigid international rulebooks risk obsolescence. Interoperability, aligned taxonomies and indicators, and compatible supervisory tools are the pragmatic way forward.
• Data localisation and extra‑territorial measures can fragment markets and impede responsible innovation; cross‑border cooperation and proportionate oversight are essential.