Day One | December 1
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
8:00
Breakfast and Networking
8:50
Chair’s Opening Remarks
 | Moderator: James SibbitHead of UK AI,d-fine |
9:00
THE AI ADOPTION GAP: WHY GOVERNANCE IS THE ANSWER, NOT THE OBSTACLE – PANEL DISCUSSION
Why are organisations investing billions in AI yet struggling to scale adoption? Understanding how governance, risk and organisational readiness are becoming the foundations of successful AI transformation
Key topics
- Understanding why AI investment is failing to convert into measurable business value
- Examining the gap between AI ambition, organisational readiness and governance maturity
- Exploring whether governance is enabling innovation or becoming a barrier to adoption
- Understanding the risks organisations introduce when scaling AI without effective governance frameworks
- Identifying the capabilities required to deploy AI safely, responsibly and at enterprise scale
- Learning how leading financial institutions are aligning AI strategy, governance and risk to accelerate adoption
 | Guillaume Figer,Chief Risk Officer,Societe Generale |
 | Ratul Ahmed,Managing Director Principal Project Manager,Commerzbank AG |
 | Dr Isabela Parisio,Postdoctoral Research Associate,Tech and Policy,King’s College London |
9:50
AI GOVERNANCE OPERATING MODELS IN PRACTICE
Session: Examining how financial institutions are building governance frameworks capable of supporting AI at scale
Key topics
- Translating governance policies into operational processes and controls
- Designing approval, escalation and oversight mechanisms for AI use cases
- Embedding AI governance into model risk, operational risk and enterprise risk frameworks
- Managing governance across multiple business units, jurisdictions and AI platforms
- Understanding where governance frameworks begin to fail as AI adoption scales
- Building governance operating models that support both innovation and control
 | Yingbo Bai,Managing Director, Head of AI Governance and Regulatory Engagement,UBS |
10:25
Morning Refreshment Break and Networking
10:55
AI GOVERNANCE AS A BOARD-LEVEL RESPONSIBILITY – PANEL DISCUSSION
Session: Understanding how AI governance is moving beyond technology oversight and becoming a core board, risk and accountability priority.
Key topics
- Understanding why AI governance is emerging as a strategic leadership and enterprise risk issue rather than solely a technology concern
- Examining what regulators expect from boards and senior management under evolving accountability and governance frameworks
- Defining the board’s role in overseeing AI strategy, risk appetite, governance effectiveness and organisational readiness
- Assessing how AI governance intersects with operational resilience, cyber security, conduct risk and reputational risk
- Establishing meaningful reporting, oversight mechanisms and challenge frameworks to support effective board-level decision-making
 | Sucharita Banerjee,Global Head of Operational Risk, ERM Reporting and Operations,AIG |
 | Cosette Reczek,former Managing Director, Global Head of Markets Model Risk,formerly of Standard Chartered Bank and NED |
11:45
BUILDING AI LITERACY: STRENGTHENING OVERSIGHT AND STRATEGIC DECISION-MAKING AT A BOARD LEVEL
TBC
| James SibbitHead of UK AI,d-fine |
12:20
Lunch Break
1:05
Lunch Break and Networking Roundtables
Roundtable
Challenges Around Responsible AI
 | Suzanne Brink,Head of Responsible AI,Lloyds Banking Group |
1:50
FROM PROMPTING TO AGENTIC VALIDATION
How AI can transform model risk validation beyond prompting: building agentic, scalable and governed workflows
Key topics
- Identifying the validation activities where generative AI creates the highest impact. Initial exploration with IFRS9 provisioning models.
- Building structured, reusable prompt frameworks to standardize validation activities
- Enhancing AI quality through controlled workflows and continuous improvement
- Scaling the approach across IRB, Credit Decisioning and ESG risk models
- Migrating to GCP and Gemini to deploy agents and workflows
- Orchestrating AI capabilities across validation and monitoring processes
- Embedding AI as a decision support tool, with validators retaining full accountability for assessments and regulatory sign-off
- Extending the approach beyond credit risk model validation
- What the rise of agentic AI means for the future role of model risk and validation teams
 | Alvaro J. Fernandez,Lead Validator – Wholesale Banking & Bankwide Chapter, Credit Risk Model Validation – Model Risk Management,ING |
2:25
AI READINESS STARTS WITH TRUSTED DATA – PANEL
How data governance and privacy leaders can work together to build AI-ready organisations, ensuring data is trusted, governed and fit for purpose from development through deployment.
Key topics
- Why AI maturity is increasingly determined by trusted, well-governed data
- Building AI-ready data foundations through governance, ownership and lineage
- Embedding privacy, Responsible AI and accountability into enterprise data strategies
- Revisiting BCBS239 and enterprise data governance through the lens of AI
- Breaking down organisational silos between Data, Privacy, Risk and Technology
 | Gagan Singh,Group Head of Data Governance,Legal and General |
 | Angela Isom,Global Chief Privacy and Responsible AI Officer,Gallagher tbc |
3:10
RESPONSIBLE AI BY DESIGN FOR HIGH RISK AI SYSTEMS:
Understanding how organisations can translate AI Act requirements into practical governance frameworks, controls and monitoring processes for high-risk AI systems.
Key topics
- The implementation of Responsible AI requires a structured process that spans from initial assessment to deployment and ongoing governance.
- The RAI Tool, an instrument used to document, assess, and control high-risk AI use cases.
- Responsible AI controls on performance, explainability, fairness, transparency, and Human Oversight.
- A practical implementation of our Responsible by design framework on real-life use cases.
- Governance controls become effective only when translated into measurable KPIs, thresholds, and monitoring activities.
- Different AI technologies require tailored validation, control, and monitoring approaches.
- Responsible by design framework as a continuous lifecycle process that extends beyond deployment through ongoing monitoring, review, and improvement.
 | Michele Grasso, AI Data Scientist,Intesa Sanpalo |
 | Annalisa Deiana,Responsible AI Data Scientist,Intesa Sanpalo |
3:45
Afternoon Coffee Break and Networking
4:15
AGENTIC AI AND AUTONOMOUS DECISION-MAKING
Session: Preparing governance frameworks for systems capable of acting independently.
Key topics
- Understanding the rise of agentic AI across financial services
- Examining how autonomous systems differ from traditional AI deployments
- Managing AI systems capable of taking actions rather than simply generating outputs
- Understanding emerging customer-facing and internal use cases
- Establishing controls for increasingly autonomous workflows
- Defining accountability when decisions become machine-led
 | Phani Ravindra Kaligotla,Vice President, Global AI and Data Strategy,Mastercard |
4:50
AI GOVERNANCE AND FINANCIAL STABILITY
Session: Assessing how AI failures could evolve from firm-level incidents into systemic financial risks.
Key topics
- Understanding the systemic implications of AI adoption across financial services
- Exploring how AI could amplify market, operational and conduct failures
- Examining the risks of correlated decision-making and model convergence
- Assessing whether AI should be considered critical financial infrastructure
- Understanding the lessons from previous systemic failures
- Identifying emerging risks regulators are increasingly focused on
5:25
Chair’s Closing Remarks
5:30
End of Day One of AI Governance in Financial Services