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.