Day Two | March 3
Here is the latest edition of the agenda. As we approach the event, we will be adding speakers and sessions to enhance your experience. To stay informed, please register your interest
Key topics
- Assessing risk ownership when a model is embedded inside a wider system
- Distinguishing AI models from analytical tools and applications
- Establishing governance structures for AI decision-making
- Classifying GenAI and agentic systems consistently
 | Chris Smigielski,Model Risk Management Director,Arvest Bank |
| William Longstreth,Director, Model Validation Head,MUFG |
Key topics
- Evaluating reliability, consistency and reproducibility for AI outputs
- Assessing agent behaviour across dynamic workflows
- Establishing testing methodologies for evolving AI systems
- Creating evidence supporting trustworthy AI deployment
| William Longstreth,Director, Model Validation Head,MUFG |
Key topics
- Detecting model drift and changing performance characteristics
- Monitoring agent behaviour in production environments
- Establishing continuous assurance capabilities
- Defining escalation processes for emerging AI risks
| Ali Joukar,SVP Model Risk Management,Citi |
Key topics
- Governing alternative and synthetic data sources
- Establishing robust data lineage practices
- Managing transaction-level, alternative and rental data used in AI models
- Managing fairness, transparency and data quality
Key topics
- Leveraging automation to support governance activities
- Streamlining review processes for large AI inventories
- Implementing governance technology and workflow tools
- Increasing oversight capability without proportional headcount growth
| Christophe Rougeux,Head of AI Risk Management,TD Bank |
| Arnold Pashi,Senior Director, Head of Model Governance,First Citizens Bank |
| Ron Eisenberg,Head of Operational Risk,Citibank Canada |
| Matthew Moore,Director, Operational Risk,Barclays |
| Phil Gledhill,Supervising Examiner, Operational Risk and Resilience,Federal Reserve Bank of New York |
| Sandeep Maira,Former Head, Enterprise Risk, Compliance and Reg Technology,BNY Mellon |
Key topics
- Challenging proprietary model methodologies independently
- Establishing third-party validation approaches
- Balancing vendor confidentiality with regulatory expectations
- Monitoring third-party and fourth-party AI risks
| Tarun Joshi,Head of Enterprise GenAI & Agentic AI Model Risk Management,Wells Fargo |
Key topics
- Balancing constructive engagement with effective oversight
- Building credibility with first-line model developers
- Applying challenge proportionately in a risk-based environment
- Measuring whether challenge is improving decisions
| Sumit Gupta,Quantitative Risk Model Development Lead,Citi |
| Riten Dixit,Head of Financial Risk Management,Federal Home Loan Bank of Cincinnati |
Key topics
- Models as an attack surface: adversarial manipulation, IP theft and data poisoning
- Understanding the overlap between model risk, fraud risk and cyber risk
- Managing privacy, transparency and explainability obligations
- Monitoring vulnerabilities across AI deployment environments
- Defining where system risk extends beyond traditional model risk frameworks
| Lee Medoff,Head of Model Management,Fifth Third Bank |
Key topics
- How language models represent language, and what that means for explaining a decision
- The spectrum of controls in use today, and what each is and isn’t designed to do
- Designing control into the architecture: concepts over tokens, governance inside generation
- A practical lens for evaluating AI systems within a model risk framework
| Joe Breeden,Chair,Model Risk Managers’ International Association |