Opening & Welcome
Andrew Delaney, President & Chief Content Officer, A-Team Group

Opening Keynote: Replacing Legacy Infrastructure with AI – The ProxyIQ Journey

• A candid practitioner account of building and launching an AI-powered platform to modernise proxy voting and challenge a legacy market structure.
• What does it truly take to replace entrenched third-party infrastructure with a proprietary AI platform in a highly regulated environment?
• How can AI be designed to support trust, explainability and accountability in high-stakes investment decision workflows such as proxy voting?
• What organisational, data and model risk barriers must be overcome when scaling AI beyond experimentation into production?
• Where does measurable ROI actually come from when disrupting a legacy market — efficiency gains, risk reduction, control or competitive differentiation?
• What practical lessons can other capital markets firms apply when moving from stealth innovation to enterprise-wide transformation?

Nirav Shah, Senior Executive Director, Head of Fundamental Research & Sustainable Investing Tech, J.P. Morgan Asset Management

Panel: Requirements for safe, scalable AI deployment

• What does an effective AI Control Plane look like in practice, and how does it provide visibility, monitoring and intervention across multiple models and agents?
• How can firms establish clean, standardised data models that reduce fragmentation and ensure consistent, trusted inputs across AI use cases?
• What practical guardrails and kill-switch mechanisms must be in place to prevent model drift, hallucination, unintended actions or unauthorised access?
• What does real AgentOps discipline involve when managing autonomous or semi-autonomous AI systems in production environments?
• How should firms embed integrated compliance and risk oversight into AI deployment from day one, rather than retrofitting governance post-launch?

Moderator: Andrew Delaney, President and Chief Content Officer, A-Team Group
Natalia Konstantinova, Lead Enterprise Architect in AI, NatWest
Ramy Erfan, Vice President, Business & Technology Transformation,Citi
Frank Callaly, Chief Technology Officer, Neueda
Dug Paton, Principal Customer Solutions Engineer, ITRS Group
Usman Khan, Founder & CEO, APEX:E3

Keynote: From pilot to production: closing the AI capability gap in Capital Markets

• Why the gap between AI deployment and AI capability is widening in capital markets, and why it is a people problem, not a technology problem
• How the regulatory environment – model risk obligations, the EU AI Act, and growing FCA/PRA scrutiny – makes the capability gap a governance and compliance risk, not just a productivity one
• What effective enterprise-wide AI upskilling actually looks like beyond one-off training; embedding capability into workflows, operating models, and leadership behaviour
• Why the shift to agentic AI makes the capability decisions firms take now the defining factor in who leads and who lags in the next phase of financial services AI

Frank Callaly, Chief Technology Officer, Neueda

A-Team Group Research Report Update

Andrew Delaney, President and Chief Content Officer, A-Team Group
Mark Davies, Partner, Element22

Panel: AI ROI – A repeatable framework

• How can the industry align on a repeatable AI Return on Investment framework that satisfies Chief Financial Officer scrutiny and enables reliable benchmarking?
• Beyond a data lake, what is the most effective architecture for ensuring data quality and alignment to deliver consistent Return on Investment?
• In managing the build vs buy decision, how should technology leaders calculate the capital allocation tradeoff between developing proprietary Alpha generating tools versus licensing vendor solutions for standard functions?
• What are the key organisational changes required to turn technologists into value measurers, and how should accountability for AI performance be structured across the business and technology teams?
• What tangible metrics for risk reduction are most effective for demonstrating Return on Investment?
• How do firms balance the need for competitive speed in deployment with necessary investment in foundational security when budgets prioritise short term efficiency gains?

Moderator: Nilesh Khatri, Former Director, Macquarie Bank
Konstantina Kapetanidi, VP, Global Data Solutions & Head of Data Science, Europe, (VCA)/ Visa
Jacek Wieclawski, Head of Innovation, Markets, Rabobank
Prerit Ahuja, Director, Global AI & Data Strategy, DNB Carnegie

Panel: How to achieve AI data readiness

• How can Buy Side and Sell Side firms shift their data strategy from storage-led architectures to trusted, standardised data models that enable scalable AI?
• What is the most effective architecture: data fabric, centralised lake, or hybrid for unifying proprietary and vendor data without compromising governance and control?
• How can AI be leveraged to automate data lineage, quality monitoring and anomaly detection across complex, distributed environments?
• How should firms balance broad AI data access for quants and innovation teams with strict regulatory, privacy and security requirements?
• What organisational, skills and cultural changes are required to build production-ready AI data foundations and achieve a truly unified client view?

Moderator: Tina Salvage, Independent Data, AI & Governance Advisor
Duncan Cooper, Founder, Duruedma Consulting and Fractional CDO
Matthew Cheung, CEO, Ipushpull
Stéphane Rio, CEO & Founder, Opensee
Nicolas Hourcard, Co-Founder & CEO, QuestDB

Keynote: AI sovereignty and privacy in regulated capital markets.

• Historically, financial institutions treated their technology stack and proprietary data as closely guarded strategic assets. What has changed in the AI era, and why are firms becoming more willing to expose these assets to external platforms and models?
• As organisations increasingly adopt frontier AI solutions, what are they potentially giving up when code, data, intellectual property, or decision-making processes move outside the firm’s perimeter?
• What does sovereignty mean in the context of institutional finance, and why are issues such as data ownership, model control, and intellectual property becoming strategic concerns as AI adoption accelerates?
• What does a practical sovereign AI strategy look like, and how can firms capture the productivity benefits of agentic AI while maintaining control over their data, models, and decision-making processes?

Usman Khan, Founder & CEO, APEX:E3

Keynote: From margin expansion to market re-rating: The AI strategy financial institutions are missing

• How can asset managers scale AI across the enterprise to deliver material, measurable business value rather than isolated efficiency gains?
• Why does enterprise AI transformation matter now, and what are leading firms doing differently to shift their market positioning?
• What differentiates organisations that successfully scale AI from those that stall at pilot stage?
• What does the target-state “Frontier Asset Manager” look like, and which capabilities are required to achieve structural margin expansion and market re-rating?

Symon Garfield, Director Digital and AI Advisory – Capital Markets, Wealth and Asset Management, Worldwide Financial Services, Microsoft

Panel: Governing, securing and managing risks in AI in production

• How must Model Risk Management frameworks evolve to validate non-deterministic, agentic and unofficial AI systems in production?
• What does “good” look like for explainability, auditability, and control evidence when facing regulatory scrutiny?
• How can firms translate governance principles into enforceable controls embedded within AI development and deployment pipelines while detecting and managing Shadow AI activity?
• What new risks do AI systems introduce such as model manipulation, data leakage, and deepfakes and how should firms mitigate them?
• What does effective AI stress testing and incident response look like, and who should own these processes across the organisation?
• How can firms navigate diverging global regulatory expectations while maintaining a consistent and scalable AI governance strategy?

Moderator: Katsuko Ishizeki-Chaudhari, Independent Regulatory Consultant & Former Managing Director, Bank of Montreal London Branch and BMO Capital Markets Ltd.
Ramy Erfan, VP AI Governance & Enterprise Transformation, Citi
David More, AI Program Director – Responsible AI & AI Strategy, Chief Data and Analytics Office, NatWest Group
Carol Wilson, AI Ethics and Governance Fellow of Information Privacy, Royal London
Jay Taylerson, VP of Engineering, Genesis
Michael Talbert, Head of Professional Services, Behavox

Case study: Real Time Anomaly Detection and Triaging in Trade Surveillance

• What are the key limitations of traditional rule-based surveillance in managing false positives and investigation workload?
• How is AI being applied to improve anomaly detection and triaging, and what has changed in practice?
• What data foundations were critical to making this work?
• How are AI-driven alerts made explainable and auditable for compliance and regulatory scrutiny?
• What measurable impact has this delivered in terms of false positive reduction, efficiency, and detection quality?
• What lessons have been learned when deploying AI in a live surveillance environment?

Interview with: Jason Shiu, Surveillance Compliance Manager, Vanguard
Interview by: Justin Nathan, Global Head of Surveillance, DimeTrades

Future focused Keynote: Quantum Computing for Capital Markets from research to measurable trading impact

A practitioner-led view on where quantum computing is already showing measurable value during trials in capital markets, using HSBC’s real-world work with IBM in corporate bond algorithmic trading. The session will cut through hype to explain what hybrid quantum-classical approaches can do today, where the first scalable use cases will land, and what firms should be doing now to prepare for the next wave.

• What has quantum computing already proven in real markets today?
• Where will quantum deliver the first scalable advantage in capital markets?
• What should firms be doing now to prepare without over-investing too early?

Dr. Del Rajan, Vice President, Quantum Technologies, HSBC Group

Champagne Roundtables

Join a roundtable discussion for a deep-dive, interactive discussion with your peers on some of the day’s most important themes. Address common problems, benchmark your progress and come away with practical solutions and takeaways!

1: Moving from AI pilots to an enterprise AI operating model

• What’s the biggest blocker to scaling AI today: data, governance, skills, or leadership alignment?
• What does “AgentOps” actually look like in practice (ownership, controls, monitoring, change management)?
• Where should accountability sit for AI performance and risk: the business, technology, or a central AI governance function?

Host: Badri Santhanam, Global Ambassador, Global Council for Responsible AI (GCRAI)

2: Building trust in AI for finance: Governance, explainability & decision intelligence

• How should firms govern GenAI and agentic systems beyond traditional model risk frameworks?
• What level of explainability is needed when AI influences material business decisions?
• How can firms create trust with boards, regulators, and users without slowing innovation?
• What practical controls separate scalable AI programmes from perpetual pilots?

Host: Jovita Tam, Business-focused Data/AI Advisor & Attorney

3: AI in buy-side trading & portfolio risk: from signal to decision

• Where is AI delivering the most measurable value today: signal generation, portfolio construction, or trade execution?
• How are firms validating AI-driven signals and managing risks such as overfitting and model drift?
• How is AI reshaping portfolio risk management, scenario analysis, and real-time monitoring?
• How should firms balance AI-driven insights with human judgement, accountability, and governance over investment decisions?

Host: Shezad Lakha, CFA, Founder, Quantology Solutions
Host: Catherine Baulman, Founder & Director, BlackRobin Group

4: How to use AI in post trade and T+1

• Where is AI delivering the most practical value today across post-trade operations: matching, settlements, reconciliations, exceptions, or fails prevention?
• How can firms use AI and automation to reduce settlement risk, manual touchpoints, and operational pressure in a T+1 environment?
• What data quality, workflow, and control foundations must be in place before AI can be trusted in critical post-trade processes?
• How should buy-side, sell-side, custodians, and market infrastructures prioritise AI use cases ahead of the UK and European move to T+1?

Host: Pete Tomlinson, Managing Director, Equities Trading and Post Trade, AFME

5: Model risk governance for GenAI: audit readiness and explainability

• How should Model Risk Management evolve for non-deterministic systems (GenAI, agents, multimodal models)?
• What evidence do you need to produce for regulators: explainability, lineage, testing, human oversight, or all of these?
• How are firms dealing with the EU AI Act vs UK/US approaches in a way that doesn’t slow delivery to a crawl?

Host: Mike Fox,  Independent Technology Auditor and IT Risk Management Consultant

6: AI-enabled fraud, deepfakes & trust: protecting firms from the next generation of financial crime

• How are firms preparing for AI-enabled fraud, deepfake impersonation, and synthetic identity attacks?
• What new controls are needed to detect AI-generated threats that traditional fraud and cyber frameworks were not designed for?
• What role can AI play in detecting manipulated communications, anomalous behaviour, and emerging fraud typologies?
• How should responsibility for AI-related fraud and security risks be shared across financial crime, cyber, and AI governance teams?

Host: Colin Whitmore, Financial Crime SME, Consultant, AI Innovation, Strategy & Design.

Ends 

Agenda subject to change