Skip to main content
Macro shot of an iris

AI Governance – From Concept to Compliance

Learn how an effective AI governance framework is key to an organization’s AI strategy.

Whether to enhance efficiency, personalize customer service, or strengthen risk management, financial institutions leverage artificial intelligence (AI) to stay competitive. Still, AI program maturity varies greatly from institution to institution, as many AI solutions and technologies are still up and coming. While some financial institutions implemented mature AI governance programs, others do not have AI models in their model inventory.

Regardless of the maturity of the AI program, all key stakeholders have a similar goal: to be more proactive in governing AI. Effective governance is critical as financial institutions transition to AI-driven organizations. Doing so can help ensure ethical and secure deployment of AI, mitigation of potential risks, and prioritization of compliance with regulatory standards.

What Is AI Governance?

Effective AI governance calls for well-thought-out collaboration across disparate functions, including—but not limited to—compliance, IT, data, model risk management (MRM), and cybersecurity. An overall governance framework relies on processes, standards, and guardrails that cover the life cycle of an AI system, which incorporates use case definition, data gathering, modeling and learning, deployment, business use, and monitoring. Throughout the AI life cycle, this framework should address risks related to algorithmic biases, model transparency, data privacy, cybersecurity, or changing regulations, among others. When developing their own internal processes, understanding the key pillars of an AI governance framework is essential for financial institutions.

Read how AI & tech may shape 2025:

Financial Executives International (FEI Report)

Developing a Solid Framework – Insights

As AI transforms the financial sector, establishing robust governance frameworks becomes a strategic imperative. For each pillar of the AI framework, organizations should take the steps needed to:

  • Ensure explainability by adopting interpretable AI models and enhancing transparency with robust documentation;
  • Maintain data integrity by implementing robust data validation and security measures while actively mitigating biases;
  • Commit to responsible AI and align AI development with ethical standards and regulations compliance; and
  • Foster a culture of accountability to build trust in AI solutions.

It is essential to utilize a comprehensive governance process that spans the entire AI life cycle, from use case definition to development or off-the-shelf purchase, followed by validation, IT integration, and the transfer to business owners. Financial institutions can harness AI’s full potential while mitigating risks by focusing on the pillars of explainability, data integrity, ethical practices, regulatory compliance, and accountability. Embracing these pillars will help to ensure integrity, build trust, and drive innovation.

If you have any questions about AI governance or need assistance, please reach out to a professional at Forvis Mazars.

For more information on the topic of AI, see our FORsights article, “AI & Machine Learning Model Development Considerations” and sign up for our upcoming webinar on “Auditing the AI Lifecycle.”

Related FORsights

Like what you see?
Subscribe to receive tailored insights directly to your inbox.