AI Projects in Financial Supervisory Authorities

IMF Working Papers, October 3, 2025

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AI Projects in Financial Supervisory Authorities

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Bibliographic details

• Authors: Parma Bains, Gabriela E Conde, Rangachary Ravikumar, Ebru S Iskender
• Published: October 3, 2025
• Series: IMF Working Papers
• DOI: https://doi.org/10.5089/9798229025263.001

Overview

• Purpose: Assist financial supervisory authorities in safely and effectively overseeing applications of Artificial Intelligence in the financial sector by proposing a tailored project management methodology for implementation of Artificial Intelligence by financial supervisory authorities that address unique risks and align initiatives with strategic goals.
• Context: Responds to the growing digitalization of financial services and the imperative for supervisory authorities to enhance their toolkit through the adoption of Artificial Intelligence.
• Emphasis: Stakeholder collaboration, explainability, bias mitigation, robust governance frameworks, and adequate resources.

Key findings and themes

• Adoption imperative:
  • Financial supervisory authorities need to enhance their toolkit through AI in response to digitalization of financial services.
• Tailored methodology:
  • A project management methodology is proposed specifically for implementation of Artificial Intelligence by financial supervisory authorities to address unique risks and align initiatives with strategic goals.
• Risks and challenges:
  • Ensuring explainability.
  • Mitigating bias.
  • Managing unique risks associated with financial supervision applications of AI.
• Enablers and prerequisites:
  • Robust governance frameworks.
  • Adequate resources.
  • Stakeholder collaboration.

Project management methodology (high-level components described)

• Alignment:
  • Align AI initiatives with strategic goals of the supervisory authority.
• Risk focus:
  • Incorporate measures to address explainability and bias.
• Stakeholder engagement:
  • Emphasize collaboration with relevant stakeholders throughout design and deployment.
• Governance and resourcing:
  • Establish robust governance frameworks and ensure adequate resources for deployment and operation.

Policy recommendations and operational priorities

• Prioritize development of governance frameworks tailored to AI use in financial supervision.
• Allocate adequate resources (human, technical, and financial) to support AI projects end-to-end.
• Implement practices to ensure explainability and to detect and mitigate bias in AI systems.
• Foster stakeholder collaboration across public and private sectors to inform design, validation, and oversight.

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