Guidance Note on Consumer Protection and Responsible AI/ML Use by Licensed Financial Institutions in the UAE

CBUAE Classification: Public
Guidance Note on Consumer Protection and the Responsible Adoption and Use of Artificial Intelligence and Machine Learning by Licensed Financial Institutions in the U.A.E.

CBUAE Classification: Public
Table of Contents
1 Definitions .........................................................................................................................................2
2 Governance and Accountability...............................................................................................................3
3 Fairness/Non‑Discrimination and Ethics..............................................................................................3
4 Transparency and Explain ability ...........................................................................................................3
5 Data Quality, Privacy and Security........................................................................................................4
6 Continuous Monitoring and Review.......................................................................................................4
7 Human Oversight and Consumer Protection .....................................................................................5
8 Integration with Existing Frameworks..................................................................................................5
9 Outsourcing and Third‑Party Risk ..........................................................................................................6
10 Ethical Collaboration and Innovation....................................................................................................6

CBUAE Classification: Public
Guidance Note on Consumer Protection and the Responsible Adoption and Use of Artificial Intelligence and Machine Learning by Licensed Financial Institutions in the U.A.E. This Guidance Note outlines principals and guidelines for the use of Artificial Intelligence (AI) systems and Machine Learning (ML) technologies by licensed financial institutions, including insurance providers (LFIs), in the United Arab Emirates (the UAE). It is focused on areas that may have bearing on consumers, with the aim of promoting consumer protection and good market conduct by all LFIs in their use of AI/ML. Given the continued and rapid development of AI and ML, both generally and as adopted by LFIs, these broad principles and guidelines should not be taken to be static or fixed, but rather as flexible principles to be used to guide and inform responsible AI/ML use over time as the sector, deployment and use of AI/ML develops. The objective of this Consumer Protection Guidance Note is to encourage a culture of responsible and ethical use and transparency with a focus on the end user in the development, deployment and use of AI and ML technologies, models and systems by LFIs, particularly in relation to transparency in decision making, bias in decisions, ethical matters, accountability and explainability and data privacy. It is expected LFIs will refer to this Guidance as they develop their own internal policies and guidance on the ethical and responsible use of AI and ML with respect to consumers. This guidance should be read in conjunction with the UAE Charter for the Development & Use of AI, published in July 2024, the UAE National Strategy for AI, and the Central Bank Guidelines for Financial Institutions adopting Enabling Technologies. 1 Definitions For the purpose of this Guidance Note, the following terms shall have the meanings set forth below:

• Artificial Intelligence (AI) means computer or machine-based systems or
  models that perform tasks or generate outputs normally requiring human intelligence, including learning, reasoning, understanding, and interacting and GenAI (as defined below) which may have varying levels of autonomy and/or the ability to learn from data and adapt.
• GenAI means artificial intelligence models that can understand and generate
  human like text, audio and images by leveraging vast amounts of training data and deep learning techniques, including popular Large Language Models.
• Machine Learning (ML) means a subset of AI that enables systems to learn,
  both as supervised and unsupervised learning, from data and improve performance without being explicitly programmed or instructed to do so.
• High‑impact decision means any determination by an LFI using AI that
  materially affects a customer’s access to financial products or services, for example in respect of a potential loan application or insurance claim.
• MMS means the Model Management Standards, as issued by the Central
  Bank of the UAE.

CBUAE Classification: Public
2 Governance and Accountability a. LFIs should 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 use of AI with an understanding of the risks of it should be promoted in LFIs. This Guidance should be read in conjunction with all relevant CBUAE regulations and standards. The governance, usage and validation of AI use in LFIs should follow the principles of the MMS. b. Senior management and the Board of Directors of LFIs should be responsible and accountable for AI and ML systems and outcomes, model selection/development, deployment, accountability, appropriate human resourcing and oversight and monitoring and management on an on-going basis, and more specifically, LFIs should not employ AI models that they have no control over.
c. Regular reporting should be required by and provided to Senior Management and
Boards of LFIs, covering performance and risk. d. Governance structures should facilitate informed decision‑making, enable the identification and mitigation of risks and ensure that AI and ML systems and applications are aligned with the institution’s risk appetite and legal obligations. AI‑related risks should be incorporated into the institution’s governance framework in a cohesive and consolidated manner, including with specific adaptable roles and responsibilities for the Audit and Risk Committee, Risk Management, Internal Audit, and IT. e. Boards and senior management should ensure that risk committees and control functions (e.g., compliance, internal audit and risk management) understand AI‑driven processes and can challenge outcomes where appropriate, with the adoption, deployment and use of AI being an integral part of the Risk Management framework. 3 Fairness/Non‑Discrimination and Ethics a. LFIs are expected to ensure that AI and ML systems do not result in discriminatory or manipulative outcomes against individuals or groups. No AI system or application should be deployed or used if it is discriminatory or manipulative or develops as such post-deployment. b. Data used to train AI and ML models should be sufficiently accurate, relevant and representative of the customer populations to which the models will be applied.
c. AI deployed should be subject to periodic testing, i.e. once a year or each time
a model is upgraded, materially changed or a new one is introduced, to identify and remediate undue/unintended embedded biases or discriminatory outcomes. The deployment of AI should reflect the institution’s ethical standards and code of conduct. Decisions made or supported by AI should be consistent with the duty to act honestly, fairly and in the best interests of consumers. 4 Transparency and Explainability a. LFIs should be transparent with customers and relevant stakeholders about the use of AI, particularly in respect of high‑impact decisions, and if they are communicating or interacting with an AI application. LFIs should be clear as to how AI systems operate and make decisions and be able to disclose the same. b. Understandable plain language and accurate disclosures should be made in both Arabic and English, with telephone support available in all major languages of the UAE. LFIs should consider the use of appropriate measures to check understandability.

CBUAE Classification: Public
c. LFIs should consider the provision of opt-out rights with respect to AI for
customers, particularly for High-impact decisions, taking into account the potential risks or impact to the customer, fairness and feasibility. 5 Data Quality, Privacy and Security a. LFIs should establish policies to ensure AI and ML models use accurate, relevant, and up-to-date data, with clear provenance and audit trails. b. LFIs should ensure that data used in AI and ML models is of sufficient quality and relevance and is updated as necessary, in compliance with all relevant standards, laws and regulations including, where applicable, the UAE Personal Data Protection Law and the UAE Information Assurance Regulation.
c. Personal data should be collected, stored and used in compliance with applicable
laws and regulations, including the Consumer Protection Standards and rules on data being retained in the country, and only for purposes that are legitimate and proportionate and in respect of outsourcing LFIs should see section 9 below. d. LFIs should incorporate privacy‑by‑design and security‑by‑design principles into AI systems and maintain safeguards to protect data from unauthorised access or misuse. Robustness and safety should be integral to AI development. AI should be subject to stress testing and validation to ensure they operate reliably under a range of scenarios and do not produce unsafe or unintended outputs. Institutions should incorporate operational resilience measures—such as redundancy, contingency planning and incident response—to minimise disruption or harm to consumers from system failures or cyber-attacks. e. LFIs should assess and try to utilise, where feasible, AI to identify potential fraud or criminal activity, anti-money laundering disparities or issues and potential suspicious activity through trends and patterns and in the event material findings are identified, comply with their legal, regulatory and reporting requirements. 6 Continuous Monitoring and Review a. In accordance with the MMS, AI should be subject to continuous monitoring to ensure ongoing understanding, reliability, relevance and alignment with consumer protection objectives. b. LFIs are expected to consistently monitor and review and, where appropriate, update or cease using AI and ML models, taking into account changes in data, market conditions and customer behaviors. Independent third-party providers of AI, independent experts and third parties (willing to challenge and question the use of AI in an LFI) should be engaged periodically to assess the development of and use of AI including third-party AI providers.
c. LFI should ensure that automatic updates their AI tools are tested before implementation.
LFI should be fully aware of such updates. These updates should not result in bias in the model output. d. Mechanisms should be in place to detect, report and remediate any performance issues, biases or unintended consequences that may arise from any AI tool or model before implementation and over time. e. LFIs should remain responsible for outsourced AI functions and should consider:
appropriate contractual rights with respect to audit and information rights from providers, be made aware of any material developments with the AI provider, appropriate termination/cease provisions, data protection, cyber security, performance guarantees, its compliance with laws/regulations and standards and any material developments with regard to the AI being outsourced/utilized.

CBUAE Classification: Public f. LFIs should at all times retain the clear and immediate ability, with human intervention, to cease use of an AI model system, technology or application deployed or utilized. g. LFIs should ensure they have systems in place to keep up to date with legal, third-party provider and market developments with respect to the use of AI. 7 Human Oversight and Consumer Protection a. LFIs should ensure that AI and ML systems operate under meaningful human oversight and judgement, particularly for decisions that have significant implications for consumers and in respect of the ongoing selection of, determination as to third party providers of, deployment of and ongoing monitoring and general use of AI. Human oversight may be exercised through different models: (i) Human‑in‑the‑loop – where a AI provides recommendations but a human decision maker retains full authority to approve or reject the outcome; (ii) Human‑on‑the‑loop – where the AI works autonomously for routine tasks, while a human monitors outcomes and can intervene where necessary; and (iii) Human‑out‑of‑the‑loop – where the AI operates without direct human involvement, which should only be utilised for low‑risk, non‑material processes with appropriate controls in place. b. The level of human involvement should be commensurate with the identified and potential risks posed to a consumer by any AI.
c. Consumers should be able to request human review or explanation of AI
generated decisions, and alternative arrangements should be available where a customer does not wish to be subject to an AI decision. LFIs should maintain clear and accessible channels for complaints and redress in line with Article 8 of the Consumer Protection Regulation. Consumers should be informed of their right to challenge decisions, correct inaccurate data inputs having impact on AI and the process to challenge data and decisions by AI. A clear complaints-handling procedure/policy should be created, provided and accessible by customers on a regular basis. Complaints should be addressed in person, efficiently, confidentially and in as short at time as is reasonable in the circumstances. d. The design and deployment of AI and ML systems should promote fair and equitable treatment. AI should not be used to target consumers with unsuitable products or to engage in pressure‑selling or misleading marketing. Institutions should ensure that promotional materials and chatbots comply with disclosure requirements, all in line with the Consumer Protection Regulation’s emphasis on good disclosure. 8 Integration with Existing Frameworks e. AI tools should be integrated in the enterprise-wide risk management framework of any LFI that is utilising AI. This will ensure effectiveness of AI management for the purpose of Consumer Protection and for the purpose of other decisions taken by LFI.AI risk assessments should not operate in isolation but should inform and be informed by the institution’s overall risk appetite and controls. a. Senior management should ensure that policies and procedures for AI adoption complement, rather than duplicate, existing regulatory obligations under the Consumer Protection Regulation and other CBUAE directives. For example, consumer risk arising from AI‑driven models should be treated as part of the conduct risk framework, with appropriate reporting to the board and regulators.

CBUAE Classification: Public b. Where an LFI is developing AI internally, it should consider the use of third-party independent reviews to check suitability, security and reliability.
c. LFIs should create processes to rate the risk of each AI
system/application/technology they deploy or use, to enable appropriate risk assessment, monitoring and management of the AI whilst deployed in use and developing, which may be influenced by data quality and sensitivity, capability of the AI, controls in place, impact of the AI and dependence on the AI and/or third parties in the use of the AI. 9 Outsourcing and Third‑Party Risk a. In line with section 4.7 of the MMS and the Outsourcing Regulation for Banks (to the extent applicable), where LFIs rely on third‑party vendors or cloud service providers for AI and ML models, products or solutions, due diligence should be conducted on the provider’s reputation in the field of AI, governance, security and data‑protection practices. Contracts should include provisions that ensure access to relevant information, audit rights and compliance with CBUAE requirements. b. The procurement, choice and justification for selection of a third-party AI provider and deployment of their AI models/systems/technology at an LFI should be appropriate and documented, which should include annual cybersecurity reviews by independent and suitably qualified third parties and pre-deployment tests and checks to ensure appropriateness and taking into accounts needs, risks and the options available.
c. Institutions should maintain an inventory of AI models, including those developed
or hosted by third parties, and aim to ensure that third‑party models adhere to the same standards of fairness, explainability and robustness as in‑house models. d. LFIs should consider and try to utilise a range of AI providers if feasible, to try to ensure there is no over reliance on one AI system or provider. 10 Ethical Collaboration and Innovation LFIs are encouraged to collaborate with industry peers (and participate in the UAE AI sandboxes and the Innovation Hub in relation to AI), academia, the CBUAE and other stakeholders to share best practices and to contribute to the development of industry standards for trustworthy AI. LFIs should publish case studied on AI development, responsible use and its interaction with customer care and good market conduct, including any relevant examples which may be provided anonymously. This Guidance Note is intended to assist LFIs in developing and maintaining policies and procedures for AI that promote responsible innovation, good market conduct and the protection of consumers. LFIs are encouraged to stay informed of developments in AI technologies and to engage with the CBUAE where guidance or clarification is required. This Guidance Note shall supplement and not replace any laws, regulations or directives issued by the CBUAE or other competent authorities, and LFIs remain responsible for complying with all applicable laws, regulations and requirements.