Skip to main content
About Us

Artificial Intelligence Risk Management Considerations

March 6, 2024

As financial institutions look to adopt artificial intelligence (AI) to drive innovation and efficiency, it is critical to establish robust risk management and governance programs. With regulators intensifying their focus on appropriate risk management for AI, institutions must proactively address AI technologies’ unique challenges and opportunities. To ensure effective AI risk management and governance, we recommend internal audit and risk managers consider the following:

Establishing a Comprehensive AI Governance Program

A comprehensive AI governance program is necessary to manage AI risks effectively. The program should outline the institution’s AI initiatives, policies, procedures, and oversight process, ensuring alignment with the institution’s strategic objectives and risk appetite. Key elements include:

  • Leadership and Governance: Designate AI oversight roles, ensuring senior management oversees AI initiatives and embeds ethical principles into AI strategies.
  • Ethical AI Use: Adopt ethical-use guidelines that consider fairness, transparency, accountability, and privacy, ensuring AI applications respect customer rights.
  • Regulatory Compliance: Stay abreast of evolving AI regulations and standards, integrating compliance requirements into the AI development and deployment lifecycle.

Integrating AI Risk Management into Enterprise Risk Frameworks

AI risk management is integral to the institution’s overall enterprise risk management (ERM) strategy. The integration with ERM enables a holistic view of risks across the organization, including those associated with AI. Key strategies include:

  • Risk Identification and Assessment: Implement processes to identify and assess AI-specific risks, such as algorithmic bias, data integrity issues, and cybersecurity threats.
  • Risk Mitigation Strategies: Develop and apply appropriate controls and mitigation strategies to address identified risks, including data protection measures, model validation processes, and ethical AI reviews.
  • Monitoring and Reporting: Establish continuous monitoring mechanisms for AI risks and incorporate AI risk metrics into regular reporting to senior management and relevant governance bodies.

Practical Steps for Implementing AI Governance and Risk Management

To operationalize AI governance and risk management, financial institutions should take the following practical steps:

  • Develop AI Policies and Procedures: Create comprehensive policies and procedures that define the governance, development, deployment, and monitoring of AI systems.
  • Establish AI Oversight Bodies: Set up dedicated AI oversight committees or boards responsible for guiding and monitoring AI initiatives.
  • Conduct AI Risk Assessments: Regularly perform risk assessments to identify and evaluate AI-related risks, incorporating findings into risk management strategies.
  • Implement AI Ethics Principles: Embed ethical principles into AI project lifecycles, ensuring decisions and processes are fair, transparent, and accountable.
  • Engage with Regulators: Maintain open dialogue with regulators to understand expectations and demonstrate compliance with AI governance and risk management requirements.

By implementing a comprehensive AI governance program, financial institutions can navigate the complexities of AI adoption while maintaining trust and integrity in their operations.

To determine if your financial institution’s risk management and governance program has appropriately considered the impact of AI, consult with your Rehmann advisor for expert guidance or contact Kristy Clark at [email protected] or 248.614.6446.

Continue the discovery:
You may also like: