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?

Natalia Konstantinova, Lead Enterprise Architect in AI, NatWest
Charaf El Hami, Ph.D Group Chief Data Officer & Global Head of Data Management, Amundi
Dug Paton, Principal Customer Solutions Engineer, ITRS Group
Usman Khan, Founder & CEO, APEX:E3
Representative from Neueda

Keynote: Beyond the training event: A new model for AI up-skilling at scale.

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
Paul Barker, Chief Control Officer – Cross Controls Enterprise Technology, HSBC
Matthew Cheung, CEO, Ipushpull
Stephane Rio, CEO & Founder, Opensee
Representative from Quest DB

Sponsor Keynote – 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

Case study: Designing an AI System with Explainability and Bias Mitigation

A case study on how a lending or KYC model was tested using red teaming and bias metrics to ensure fair outcomes before production deployment.
Representative from Lloyds Banking Group

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
Badri Santhanam, Global Ambassador, Global Council for Responsible AI (GCRAI)
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
Representative from 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?

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, Former Financial Crime Director, AI Innovation, Strategy & Design, NatWest Group, Financial Crime SME Consultant

Ends 

Agenda subject to change