## AI Projects in Financial Supervisory Authorities

_IMF Working Papers, October 3, 2025_

## Source details

**Canonical URL:** [AI Projects in Financial Supervisory Authorities](https://www.imf.org/en/publications/wp/issues/2025/10/03/ai-projects-in-financial-supervisory-authorities-570625)

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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

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### 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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## Content in this bundle

- **Working Paper**
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_Source: https://www.imf.org/en/publications/wp/issues/2025/10/03/ai-projects-in-financial-supervisory-authorities-570625_