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AI Governance Demands A New Approach To Model Risk Management
At Risk Americas, Riten Dixit discusses how AI is transforming model risk management and governance. He explores the challenges of maintaining effective model inventories, the importance of tiering models based on risk and impact, and why traditional validation approaches must evolve to keep pace with increasingly complex and rapidly deployed AI systems.
Jun 25, 2026
Riten Dixit
Riten Dixit, VP, Head of Financial Risk Management, Federal Home Loan Bank of Cincinnati
Tags: Model risk
The views and opinions expressed in this content are those of the thought leader as an individual and are not attributed to CeFPro or any other organization

In this onsite interview from Risk Americas, Riten Dixit sat down with Alana Cannatella, Marketing Manager at CeFPro to explore and examine the growing challenges AI presents for model risk management. He explains why traditional model inventories are becoming increasingly difficult to maintain as organizations deploy AI models at greater scale and speed, and highlights the importance of identifying dependencies, assessing materiality, and understanding the potential impact—or "blast radius"—of individual models.

Dixit also discusses the future of AI governance, arguing that organizations must move beyond periodic validation cycles toward more continuous, risk-based oversight. He explores how AI introduces risks across multiple enterprise risk categories, including market, credit, cyber, operational, and reputational risk, and why firms need more transparent and scalable governance frameworks to manage these emerging challenges effectively.

Riten Dixit Bio

Seasoned financial risk professional with over a decade of expertise in measuring, monitoring, and modelling interest rate risk, market risk, credit risk, and balance sheet management. A leader in quantitative modelling and analytics, advancing risk assessment and strategic decision-making through innovation.

Riten Dixit
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