AI in Finance Summit schedule

• Real-world scale outcomes vs. the business cases that funded them - which financial services use cases have genuinely reached industrial production in 2026?
• The "deployment wall" and what are the specific organisational, technical, and governance triggers that cause promising AI programs to stall between PoC and scale?
• Where is the gap structural (data, talent, legacy) vs. closeable (tooling, process, governance)? Digital-native vs. incumbent differentiation
• What does "industrial-grade AI" require, and why is it a different engineering problem than most firms anticipated?

Panellists:
Roxanne Howdle-Rowe, Group Head of Data Product and Marketplace, Chief Data and AI Office — LLOYDS BANKING GROUP

Adam Niazi, Corporate & Investment Banking, Chief Operating Office, Business Manager — GLOBAL INVESTMENT BANK

Phani Ravindra Kaligotla, Vice President, Global Product AI & Data Strategy — MASTERCARD 

Ronan Brennan, Artificial Intelligence Strategist — NATWEST GROUP 

Moderator: Disha Mukherjee, Lead Data Engineer — FORD CREDIT

Every day brings promises of a new, transformational agentic use case. Meanwhile, your organisation may be struggling to move from proofs-of-concept to secure, production-grade agents that you can govern. Achieving this requires a strategic, enterprise-wide approach to operationalising and securing AI and the data it needs to be useful. In this session, Purnima Padmanabhan introduces an actionable framework designed to address the real-world challenges of securing and operationalising agents in highly regulated industries like Finance. We will explore how to maintain compliance, sovereignty and security velocity, covering topics such as:

• Agentic Runtime Considerations: Establishing guardrails, observability, and standardisation for autonomous AI agents to ensure they operate within secure, compliant perimeters.
• Data & AI Sovereignty: Balancing regulatory compliance and local control with the flexibility of an open ecosystem of models, frameworks and data sources.
• Robust, Centralised Governance: Enforcing strict controls on agent access to sensitive enterprise tools, data, and infrastructure while maintaining sovereignty.
• Defending Against AI-Enabled Threats: Strategies for compressing the vulnerability-to-patch cycle to ensure your software supply chain is protected against the growing threat of AI-driven security vulnerabilities.

Join us to learn how to operationalise agents without compromising the integrity or security of your data and enterprise systems.

• How can financial-services firms quickly identify and classify AI systems that may fall under Annex III before the 2 December 2027 deadline?
• Which governance, documentation, testing, human-oversight and third-party controls should firms put in place now to prepare for high-risk AI requirements?
• How can firms apply Article 6 guidance consistently—without over-classifying systems, missing in-scope use cases or relying on weak exemptions?
• How can UK-regulated firms align EU AI Act preparation with PRA SS1/23, FCA expectations, Consumer Duty and SM&CR—and close the gaps between them?

Sebastian Obeta, Member of European AI Alliance — EUROPEAN COMMISSION 

Dr David, Crelley, Head of Responsible AI and Data — ADMIRAL GROUP PLC

Jared Browne, Head of Privacy & AI Governance — FEXCO 

Moderator: Michael Borelli, Director — AI & PARTNERS

• Defending against "excessive agency" and unauthorised actions as autonomous systems become primary targets for cybercrime.
• Moving beyond system prompts to secure the non-human identities and "digital insiders" who manage high-value transactions.
• Strategies for securing the AI supply chain—from data poisoning and model tampering to deepfake-driven social engineering.
• Building "Defensible AI" that maintains operational integrity under the scrutiny of the EU AI Act and DORA.

• How firms discover unsanctioned AI use across SaaS, browser tools, APIs, copilots and locally built agents.
• How they replace blanket bans with approved alternatives, data controls, identity/access management, monitoring and escalation.
• What “governance by design” looks like in the ML/LLMOps lifecycle: model inventory, policy-as-code, prompt and data controls, evaluation, logging, kill switches and incident response.
• How to design meaningful human intervention for agentic workflows—who can pause, override or roll back a system, and at what trigger.
• How accountability works operationally when the model, workflow, vendor and business owner are different teams.

Panellists:  

Annapoorani Lakshmi Narayanan, Product Manager Lead, AI Research — J.P. MORGAN CHASE   

Ryan Courtier, Director of AI Strategy and Solutions Engineering — CITI

Nikolaos Louloudakis, Ph.D., Assistant Vice President - AI Engineering & Research — BARCLAYS

Kelly Moses, Managing Director, Head of Global Payment Services Legal    — HSBC

Moderator: Temi Afeye, Technical Lead, Senior AI Scientist — LLOYDS BANKING GROUP 

 Despite significant investment in AI, many organisations remain constrained by fragmented data, governance challenges, and legacy infrastructure. This workshop brings together financial services leaders to discuss how to create the data foundations required for successful AI adoption. Participants will explore the architectural, governance, and organisational considerations that underpin AI at scale, while identifying practical steps to accelerate innovation, build trust, and deliver measurable business outcomes. 

Alex Byron, Fintech Politics and Policy Expert — FGS GLOBAL 

 Michael Borelli, Director — AI & PARTNERS 

Moderator: Kaushik Chaubal, Senior Director, Investor Solutions — BLACKROCK

• Identifying the "Goldilocks" use cases for agents: tasks with high complexity but clear, programmable constraints.
• How do you design workflows with hard-coded "Circuit Breakers" and human-intervention triggers?
• Frameworks to ensure consistency across agent systems and meet internal audit and reliability standards.
• How to mitigate the risks of data leakage and "permission creep" as agents navigate internal banking silos?

• Where are AI tools being used daily vs. abandoned after rollout—and why?
• How do you redesign workflows (not just tools) to ensure AI becomes part of decision-making?
• What incentives, training, or friction points determine whether teams adopt or reject AI?