Global Trustee and Fiduciary Services Bite-Sized Issue 1 2025

5 QUICK LINKS AIFMD CRYPTOASSETS EMIR FINTECH MIFID II/MIFIR MMF NBFI OPERATIONAL RESILIENCE SUSTAINABLE FINANCE/ESG ASIA IRELAND LUXEMBOURG NETHERLANDS NORTH AMERICA UNITED KINGDOM Global Trustee and Fiduciary Services Bite-Sized | Issue 1 | 2025 • The financial services sector and government agencies further facilitate financial services- specific AI information sharing, alongside the AI cybersecurity forum recommended in Treasury’s AI Cybersecurity report, to develop data standards, share risk management best practices, and enhance understanding of emerging AI technologies in financial services; and • Financial firms prioritize their review of AI use cases for compliance with existing laws and regulations before deployment and that they periodically reevaluate compliance as needed. Treasury published the AI RFI on 12 June 2024, and received 103 comment letters in response from a variety of stakeholders, including financial firms, consumer advocacy groups, technology providers, financial technology companies, trade associations, and consulting firms. Link to the Report here FINMA Guidance on Governance and Risk Management When Using Artificial Intelligence On 18 December 2024, the Swiss Financial Market Supervisory Authority (FINMA) published its guidance on governance and risk management when using artificial intelligence (AI). The guidance draws attention to the risks associated with the use of AI and describes FINMA’s observations from ongoing supervision. In the course of its supervisory activities, FINMA says it has observed that most financial institutions are still in the early stages of development and that the corresponding governance and risk management structures are still being established. In this context, FINMA says it is drawing the supervised institutions’ attention to the need for appropriate identification, assessment, management and monitoring of the risks resulting from the adoption of AI. It is also providing information on corresponding measures that it has observed in the course of ongoing supervision. FINMA says this is intended to strengthen the reputation of the financial centre and help institutions to sustainably protect their business models against risks in a constantly changing environment by investing in a clear business strategy, a strong risk culture and governance plus proactive risk management. Link to Guidance here Regulating AI in the Financial Sector: Recent Developments and Main Challenges On 12 December 2024, the Financial Stability Institute (FSI) of the Bank for International Settlements (BIS) published a paper exploring the potential transformative impact of artificial intelligence (AI) on the financial sector, focusing on operational efficiency, risk management and customer experience in banking and insurance. The FSI paper reviews the widespread adoption of AI technologies including generative AI (gen AI) and examines the associated risks and regulatory implications. The FSI says that while AI exacerbates existing risks such as model risk and data privacy, it does not introduce fundamentally new risks apart from gen AI, which may give rise to hallucination and anthropomorphism risks. The FSI observes that most financial authorities have not issued AI regulations specific to financial institutions as existing frameworks already address most of these risks. Nevertheless, some areas require further regulatory attention, including governance, expertise and skills, model risk management, data governance, non-traditional players in the financial sector, new business models and third-party AI service providers. Link to Paper here FCA Research Note: A Literature Review on Bias in Supervised Machine Learning On 11 December 2024, the Financial Conduct Authority (FCA) published the first of a series of research notes on bias in artificial intelligence (AI). The FCA says this literature review examines available literature on bias in the context of supervised machine learning. The FCA says supervisedmachine learning generates predictions of an outcome. In a financial services scenario, an outcome could be defaulting on a credit product or making an insurance claim. From the literature reviewed, the FCA identified the following: • Data issues arising from past decision-making, historical practices of exclusion, and sampling issues are the main potential source of bias.