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Regulations & Circulars

CBUAE Guidance Note

Consumer Protection & Responsible AI/ML Adoption

Key pillars for the responsible use of AI and Machine Learning by Licensed Financial Institutions in the UAE.

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1 Governance & Accountability

Adopt a documented governance framework for AI/ML commensurate with operations
Senior management and Board responsible for AI outcomes, deployment, and oversight
Regular reporting on AI performance and risk to Board and senior management
Maintain inventory of all AI models with metadata (name, purpose, risk rating)
Risk committees and control functions must understand and challenge AI-driven processes

2 Fairness & Ethics

AI systems must not result in discriminatory or manipulative outcomes
Training data must be accurate, relevant, and representative of customer populations
Periodic testing (annually or on upgrade) to identify and remediate embedded biases
AI decisions must be consistent with duty to act honestly and in consumers' best interests

3 Transparency & Explainability

Be transparent about AI use, especially for high-impact decisions
Maintain documentation on design, training data, model assumptions, and functioning
Plain language disclosures in Arabic and English
Provide customers meaningful information on AI decision logic
Consider opt-out rights for customers, particularly for high-impact decisions

4 Data Quality, Privacy & Security

Ensure data quality, relevance, and compliance with UAE Personal Data Protection Law
Collect and use personal data only for legitimate and proportionate purposes
Incorporate privacy-by-design and security-by-design principles
Subject AI to stress testing and validation for reliability and safety
Utilise AI for fraud detection, AML, and suspicious activity identification

5 Continuous Monitoring & Review

Continuous monitoring for reliability, relevance, and consumer protection alignment
Engage independent third-party experts to periodically assess AI use
Mechanisms to detect, report, and remediate biases or unintended consequences
Retain clear and immediate ability to cease use of any AI system with human intervention
Keep up to date with legal, provider, and market developments in AI

6 Human Oversight & Consumer Protection

Meaningful human oversight: Human-in-the-loop, Human-on-the-loop, or Human-out-of-the-loop models
Level of human involvement commensurate with consumer risk
Consumers can request human review of AI decisions
Clear complaints-handling procedures accessible to customers
AI must not target consumers with unsuitable products or engage in pressure-selling

7 Integration with Existing Frameworks

Embed AI risk within enterprise-wide risk management (conduct, credit, operational, cyber)
AI policies should complement, not duplicate, existing CBUAE regulatory obligations
Consider independent third-party reviews for internally developed AI
Create processes to risk-rate each AI system based on data sensitivity, capability, and impact

8 Outsourcing & Third-Party Risk

Due diligence on vendor reputation, governance, security, and data protection
Board/Senior Management approval for outsourcing AI, with documented processes
Annual cybersecurity reviews and pre-deployment tests for third-party AI
Maintain inventory of third-party AI models with same fairness and explainability standards
Diversify AI providers to avoid over-reliance on a single system or vendor

9 Ethical Collaboration & Innovation

Collaborate with industry peers, academia, and CBUAE on best practices
Participate in UAE AI sandboxes and the Innovation Hub
Publish case studies on responsible AI use and customer care
Contribute to development of industry standards for trustworthy AI

Governance & Accountability

LFIs must adopt a documented governance framework for AI and ML that is commensurate with the size, nature, and complexity of their operations. A culture of responsible AI use with an understanding of risks should be promoted.

Senior management and the Board of Directors should be responsible and accountable for AI systems and outcomes, including model selection, deployment, and ongoing monitoring.

Key Requirements

Regular reporting on AI performance and risk to Board
Maintain inventory of all AI models with metadata (name, purpose, risk rating)
Risk committees must understand and challenge AI-driven processes
Follow CBUAE Model Management Standards (2022)

Critical Insurance Impact Areas

Claims Processing

Underwriting

Dynamic Pricing

Chatbots & Service

Fraud Detection

Risk Assessment

Recommended Action Timeline

Q2 2026

Gap analysis & governance framework establishment

Q3 2026

Policy development, AI inventory & bias testing protocols

Q4 2026

Implementation, training & system deployment

2027+

Continuous monitoring, optimization & industry collaboration

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