Unlocking AI in Banking: Balancing Innovation with Risk Management

Article

Successfully integrating AI and GenAI in banking requires balancing innovation with rigorous risk management. By establishing a robust model risk management framework, an AI Center of Excellence, and aligning AI initiatives with the wider enterprise, banks can maximize AI's benefits while mitigating emerging risks.

Aug 28, 2024

Chris Smigielski, Model Risk Director, Arvest Bank

Unlocking AI in Banking: Balancing Innovation with Risk Management

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

A strong Model Risk Management (MRM) framework is essential to ensure that AI models are accurate, reliable, and compliant with regulatory standards.
Establishing an AI Center of Excellence (CoE) provides centralized oversight, ensuring consistent application of AI best practices and effective risk assessment.
Aligning AI strategies with the broader Enterprise Risk Management (ERM) framework ensures that AI deployments support business goals while maintaining a prudent approach to risk.
Cross-functional collaboration between AI experts, risk managers, and compliance teams is crucial for creating a comprehensive and holistic approach to managing AI-related risks.