Institutional and Market Infrastructure

Environmental, Social and Governance (ESG) Ratings and Data Products Providers

The recommendations start with a proposal that regulators could consider focusing greater attention on the use of ESG ratings and data products and the activities of ESG rating and data products providers in their jurisdictions; followed by a set of recommendations addressed to ESG ratings and data products providers, setting out that they could consider a number of factors related to issuing high quality ratings and data products, including publicly disclosed data sources, defined methodologies, management of conflicts of interest, high levels of transparency, and handling confidential information; the recommendations also suggest that users of ESG ratings and data products could consider conducting due diligence on the ESG ratings and data products that they use within their internal processes; the recommendations close with suggestions that ESG ratings and data products providers, and entities subject to assessment by ESG ratings and data products providers could consider to improve information gathering processes, disclosures and communication between providers and entities subject to assessment.

Recommendations on Sustainability-Related Practices, Policies, Procedures and Disclosure in Asset Management

The recommendations aim to improve sustainability-related practices, policies, procedures and disclosures in the asset management industry, and include guidance on product disclosure, supervision and enforcement, terminology, and the relevant financial and investor education.

Principles on Outsourcing

The seven principles set out expectations for regulated entities that outsource tasks, along with guidance for implementation.

The use of artificial intelligence (AI) and machine learning (ML) by market intermediaries and asset managers

The guidance measures propose guidance that member jurisdictions may consider adopting to address the conduct risks associated with the development, testing and deployment of AI and ML.

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