Day one | November 2
Explore the latest agenda.
As we get closer to the event, we will continue to add speakers and sessions to make your experience even more valuable. Register your interest using the button opposite to receive the latest updates.
 | Lisa Kalscheur,Chief Marketing Officer,WeaveAI |
Key topics
- Governing responsibly without mature regulatory standards
- Managing inconsistent expectations across jurisdictions
- Preparing for retrospective regulatory scrutiny
- Building flexible governance frameworks that can evolve with regulation
 | George Smirnoff,Managing Director, Global Information Security,Bank of America |
 | Sabeena Liconte,Head Legal & Chief Compliance Officer,ICBC Standard Securities Inc |
 | Suemee Shin,Head of Data and AI Governance,U.S. Bank |
 | Piu Banjeree,Head of Model Risk Management,CLS Group |
Key topics
- Why traditional model governance is becoming obsolete
- Governing AI systems that learn, adapt and act independently
- Managing risks across interconnected AI workflows and vendors
- Moving beyond experimentation, to the practical implementation of decisions around agentic AI
 | Michael Mitsche,Senior Quant Manager – MRM,Fifth Third Bank |
Key topics
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Defines human oversight as a feedback loop in which a reviewer forms an independent expectation, compares it to the model’s output, decides whether to intervene, and updates their judgment over time.
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Explores how that loop breaks down through reviewer dependence, distribution drift, and stale judgment, turning meaningful oversight into little more than rubber-stamping.
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Connects these failure modes to modern AI governance and regulatory expectations, including SR 26-2, PRA SS1/23, and the EU AI Act.
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Uses the London Whale as a case study to show where oversight failed in practice and provides a practical framework for distinguishing genuine human oversight from compliance theater.
 | Nick Goble,Director of Solution Architecture Financial Services and Insurance,Domino.ai |
Key topics
- Highlighting the operational risks created by confident but inaccurate AI
- The challenges validating AI-generated decisions and recommendations
- Detecting and mitigating hallucination risks in high-impact business processes
- Designing governance controls to maintain decision integrity
 | Koosha Golmohammadi,Global Head of Applied AI/ML in Corporate Tech,JPMorgan Chase & Co |
Key topics
- Defining ownership across the AI lifecycle
- Building governance structures that eliminate accountability gaps
- Balancing centralised oversight with distributed business ownership
- Embedding accountability across risk, technology, compliance, and operations
 | Lacey Roman,Director – AI Compliance,Mizuho |
 | Raghuram Leelapalli,VP Model Risk Audit,Fifth Third Bank |
 | Jaydip Mukhopadhyay,Vice President, Data Science and Model Risk,American Express |
Key topics
- Governing data lineage, quality, and provenance as a foundation to AI governance
- Extending data protection controls and architecture to govern data sharing
- Integrating AI risk management into your existing risk framework
- Managing the novel risks of generative and agentic AI
- Promoting innovation by building AI literacy and trust across stakeholders
 | Sebastian Nordgren,Former Global Head of Privacy & AI Governance, Director,MUFG Bank |
Key topics
- How restrictive AI policies could drive unauthorized AI adoption
- Managing risks from personal AI accounts, external copilots, and transcription tools
- Identifying governance gaps between approved and actual AI usage
- Creating secure pathways that reduce incentives for Shadow AI behaviour
 | Ben Dynkin,Head of AI Governance – Global Markets,Barclays |
Key topics
- Moving beyond theory to understand how models generate outputs, make associations, and respond under real-world conditions
- Exploring the structural reasons behind confident but incorrect outputs and the implications for business decision-making
- Practical approaches to validation, monitoring, and control frameworks that improve reliability
- Safety of agentic systems, monitoring and controlling agentic execution
- Designing controls that account for uncertainty, autonomy, and evolving model behaviour
 | Ari Shater,Managing Director – Quantitative Finance, Risk, AI and Front Office AI Compliance,Barclays Investment Bank |
Key topics
- Explainability as a second output, secondary model, probing layer, or architectural feature
- Advances in interpretability research and what they reveal about model reasoning
- Explainability challenges for foundation models, RAG architectures, and multimodal systems
- Bridging the gap between research findings and governance requirements
- How explainability innovations could reshape validation and regulatory expectations
 | Joe Breeden,Chair,Model Risk Managers’ International Association (MRMIA) |