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Artificial Intelligence, Internal Audit’s Role, and Introducing a New Framework -
Guidance on Robo-advisers -
Artificial intelligence and machine learning in financial services -
Big Data meets artificial intelligence -
A Financial System That Creates Economic Opportunities: Non-bank Financials, Fintech, and Innovation -
FEAT Principles -
Artificial Intelligence: Opportunities, risks and recommendations for the financial sector -
Artificial intelligence: challenges for the financial sector -
Model AI Governance Framework - First Edition
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BIG DATA ANALYTICS IN MOTOR AND HEALTH INSURANCE: A THEMATIC REVIEW -
Ethics Guidelines for Trustworthy AI -
Principles for responsible stewardship of trustworthy AI -
Supplementary Guidance – Authorisation of Digital Investment Management (“Robo-advisory”) Activities -
General principles for the use of Artificial Intelligence in the financial sector -
High-level Principles on Artificial Intelligence -
Guiding principles for consumer protection in the use of big data and artificial intelligence
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Re-shaping Banking with Artificial Intelligence
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REPORT ON BIG DATA AND
ADVANCED ANALYTICS -
Governance of Artificial Intelligence in Finance
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The use of artificial intelligence and machine learning by market intermediaries and asset managers -
Artificial Intelligence (AI) in the Securities Industry -
Artificial Intelligence in Banking: The changing landscape in compliance and supervision
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Developing financial sector resilience in a digital world -
The Use of Artificial Intelligence and Machine Learning in the Financial Sector -
The Role of Data in AI -
MAS Veritas Initiative - Fairness
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Request for Information and Comment on Financial Institutions' Use of Artificial Intelligence, Including Machine Learning -
Artificial Intelligence Act (proposal)
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Guidelines for Financial Institutions adopting Enabling Technologies -
ARTIFICIAL INTELLIGENCE GOVERNANCE PRINCIPLES: TOWARDS ETHICAL AND TRUSTWORTHY ARTIFICIAL INTELLIGENCE IN THE EUROPEAN INSURANCE SECTOR
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Perspectives on Explainable AI in the Financial Sector
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Machine learning in risk models – Characteristics and supervisory priorities
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