CeFPro Connect

Webinar
Navigating the Future of AI in Banking
Feb 05, 2025
Chandrakant Maheshwari
Chandrakant Maheshwari, First Vice President - Model Validation Lead, Flagstar Bank
Chris Smigielski
Chris Smigielski, Model Risk Director, Arvest Bank
Tags: AI and Technology (including Fintech) Model risk
Navigating the Future of AI in Banking

AI is transforming risk management, but are financial institutions prepared to navigate its complexities? From AI literacy to governance, financial professionals must adapt to ensure compliance, fairness, and transparency in model validation. As AI adoption accelerates, understanding its risks and ethical implications is critical.

This webinar explores the challenges of responsible AI implementation, highlighting the need for continuous training, collaboration, and rigorous validation processes. Industry experts will discuss how organizations can proactively integrate AI into their risk frameworks, mitigate bias, and establish governance strategies to keep pace with evolving technologies.

Webinar agenda
  • Identifying Skills for AI and Generative AI: Exploration of current and future training needs essential for financial institutions to thrive in the AI era.
  • Addressing Skill Gaps: Strategies to manage and bridge skill gaps for effective AI model management within financial teams.
  • Future Training Needs: Anticipating how AI training requirements will evolve over the next 1-2 years and the implications for the workforce.
  • AI vs. Traditional Model Validation: Comparison of skill requirements for AI models versus traditional model validation techniques.
  • Continuous Training: Emphasis on the necessity of ongoing training programs to keep pace with rapid AI advancements.
  • Effective Training Programs: Examination of successful AI training initiatives, with a focus on comparing centralized versus decentralized training solutions.
  • Mitigating Fraud Risks: Training AI teams to proactively identify and mitigate risks posed by AI-powered threats.
  • Practical AI Risk Management: Discussion on applying theoretical concepts to real-world scenarios for robust AI risk management.
  • Governance and Reliability: Integrating technological solutions to ensure effective governance and reliability of AI models in finance.
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