Organizations are increasingly challenged to effectively manage the risks associated with their reliance on advanced decision-making tools, such as artificial intelligence and machine learning. To address these challenges, a framework for model risk management can be transformative. This approach calls for robust governance structures, rigorous validation protocols and continuous monitoring to ensure models are accurate, reliable and aligned with strategic goals. By defining clear roles and responsibilities, maintaining a centralized inventory of models, and conducting independent validations, organizations can mitigate risks tied to model inaccuracies and biases while enhancing operational efficiency.

In an era of rapid technological advancement and evolving financial complexity, prioritizing model risk management is more critical than ever. Organizations are encouraged to proactively integrate risk-aware practices into their enterprisewide frameworks to safeguard against regulatory, operational and reputational risks. Key strategies include crafting detailed policies that span the entire model lifecycle, from development to retirement and establishing ongoing risk assessments, to address emerging challenges. Ultimately, effective model risk management is not just about compliance; it’s a strategic enabler that drives informed decision making, fosters innovation and ensures sustainable growth.

Key Takeaways:

• Build a governance framework that aligns with organizational risk.
• Perform independent validations to ensure model reliability.
• Create and maintain a centralized inventory for all models.
• Cultivate a proactive approach to managing model risks.