Day Two| April 21
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 or alternatively, download the brochure.
Key topics
- Moving beyond development-time validation towards continuous assurance
- Monitoring model performance and deterioration throughout the lifecycle
- Identifying emerging limitations and unexpected behavior
- Determining how institutions can evidence continuing model fitness
 | Shehryar Humayun,Managing Director – Models, Data & AI Audit,Lloyds Banking Group |
Key topics
- Identifying risks emerging from interactions between models, data and technology
- Assessing third-party dependencies and model limitations
- Clarifying accountability when multiple components contribute to an outcome
- Determining where MRM should end and other risk disciplines should begin
Key topics
- Assessing the risk created by changes to models, data, methodology and implementation
- Understanding supervisory expectations around material model changes
- Strengthening governance and challenge throughout the change process
- Demonstrating effective validation and monitoring following implementation
- Managing regulatory approval without creating unnecessary delays or duplication
| Eleimon Gonis,Head of Credit Model Risk,Nationwide |
Key topics
- Demonstrating the business value generated by effective MRM
- Prioritising investment according to material model risk
- Assessing the return on investment from model-risk activity
- Making the case for investment to senior management and boards
| Madiha Jami,Group Head of Model Risk Management,Admiral Group Plc |
Key topics
- Defining where AI governance and model risk management should intersect
- Determining which AI use cases warrant model-risk treatment
- Applying proportionality when classifying AI use cases
- Avoiding duplicated governance across overlapping risk frameworks
- Establishing practical accountability for increasingly complex AI applications
| Jean-Pierre Marshall,Head of Model Risk Management,Handelsbanken |
| 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
- Determining what should be validated in LLMs and agentic systems
- Assessing hallucinations, emergent behavior and unexpected failure modes
- Validating retrieval-augmented and agent-based architectures
- Monitoring performance continuously rather than relying solely on point-in-time validation
- Establishing what constitutes sufficient independent validation evidence
| Peyman Khorsand,Head of Algo Trading Model AI Validation,Deutsche Bank |
Key topics
- Assessing the validation evidence provided by external model vendors
- Determining when vendor evidence can be relied upon and when independent validation remains necessary
- Evaluating vendor claims around model accuracy and performance
- Incorporating third-party model limitations into institutional risk assessments
- Avoiding unnecessary duplication while retaining accountability for model outcomes
| Peter Quell,Head of Portfolio Analytics for Market and Credit Risk,DZ Bank AG |
Key topics
- Monitoring decisions and actions throughout the execution lifecycle
- Creating effective audit trails and evidencing accountability
- Managing interactions between multiple systems and decision-making components
- Establishing appropriate controls around human-AI decision-making
- Determining accountability when decisions cannot be attributed to a single model
| Jacob Henry, Head of Transformation Risk Governance,Morgan Stanley |
| David Hookings, MD, Global Head, Models & Predictive Analytics, Standard Chartered |
| Satish Raghavan, Director, Head of Market Model Validation, Citi |
| Bernardo Veiga,Director, AI Model Risk,UBS |
Key topics
- Assessing how the role of MRM is changing
- Balancing technical expertise, regulatory knowledge and business understanding
- Redesigning operating models around changing technologies and risk
- Strengthening collaboration across first line, second line, validation and audit
- Identifying the capabilities MRM functions will need over the next five years