In data we trust? Emerging policy and supervisory approaches to AI data use in financial services
Data play a critical role in the transformation of the financial sector through artificial intelligence (AI). While challenges in data management are not new, they pose significant barriers to the wider adoption of advanced AI systems, such as generative AI (gen AI). Key concerns include data privacy, quality and security, which are further intensified by third-party dependencies and market concentration among major service providers. Effectively managing data-related risks is essential to ensure that AI adoption in financial services fosters innovation while upholding trust, resilience and financial stability.
As AI systems become increasingly embedded in core financial institutions' activities, financial authorities can support financial institutions by providing clarity about their expectations in managing data-related challenges. This paper explores the critical role of data in AI, the associated challenges and supervisory expectations for AI-related data usage, with a particular focus on gen AI. It also examines common themes in cross-sectoral obligations and guidance provided by financial authorities, while highlighting how supervisors can effectively respond to these emerging issues. Finally, it identifies areas that could particularly benefit from more tailored guidance by supervisors.
JEL classification: C60, G29, G38, O30
Keywords: artificial intelligence, machine learning, corporate governance, data governance, risk management, risk modelling
