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

Opening Keynote Fireside Interview: The challenges and opportunities of agentic AI in capital markets

• What is agentic AI and how is it different to GenAI?
• What are the use cases for Agentic AI in capital markets and how transformative will this be?
• How can capital markets realise the full potential of Agentic AI and when will the industry get there?

Interview with: Iro Tasitsiomi, Head of AI & Investments Data Science, T. Rowe Price
Interview by: Dessa Glasser, Independent Board Member, Oppenheimer & Co

User Panel: How to move GenAI from POC to production

• What applications/use cases are mature enough to move into production for internal or external use?
• What are the key challenges of moving from a proof of concept (POC) into production and how can these be addressed?
• Embedding ML Ops: How should firms address the technical complexities and workflow integrations when moving into productions?
• How should firms organize governance and teams to allow central control/oversight, whilst also allowing business units to initiate applications?
• Who is in the Generative AI team – what are the key skills and capabilities required?
• How can costs be optimized and what metrics should firms use to measure success?

Moderator: Julia Bardmesser, CDO Advisor; Adjunct Professor, NYU Stern School of Business
Erin Preston, Chief Compliance Officer & AMLCO, Wedbush Securities
Tracy Shannon Rasco, Executive Director – Head of Focused Analytics Solutions Team (FAST), JP Morgan Chase
Minerva Tantoco, Independent, AI Specialist
Vijay Bhandari, Head of GenAI Product, Point 72

Keynote: The Evolution of AI: From Additive Tools to Autonomous, AI-First Applications

• What does the shift from AI-enhanced to AI-native software mean for financial applications?
• How are agentic AI and predictive data science driving real-time performance, accuracy, and optimization?
• What does a new model of software look like—where AI not only assists but actively executes, evolves, and delivers business value?
• What can we expect from the next wave of financial software innovation built on intelligent automation and continuous optimization?

Leslie Kanthan PhD, Chief Executive and Co-Founder, TurinTech AI

Introducing A-Team Research, a subscription-based research and analysis service powered by Element22 and designed specifically for the financial data management community.

Andrew Delaney, President and Chief Content Officer, A-Team Group

Panel: Data as a differentiator – How to build a strong data foundation for trusted AI

• How can firms improve data discoverability and accessibility for continuous data curation?
• How should firms address data inconsistencies and ensure clean and high quality data sets for AI model inputs.  How can AI tools be applied to assist with this?
• What are the critical components of a robust data governance framework to mitigate data risk and support reliable and trustworthy AI models?
• What are the key skills and capabilities needed for AI and how should these be sourced and retained?Working with 3rd parties: How should firms manage external (cloud) data vs. internal (on-prem) data with regard to data & model ownership, vendor contract compliance and risks around operational resilience?
• Responsible AI: How can firms pull all of this together to ensure responsible use of AI and a positive approach to privacy and ethics?

Moderator: John Bottega, President, EDM Council
Brian Greenberg, Senior Director – Business Engagement Lead within the Enterprise Chief Data Office, BNY
Marla Dans, Former Head of Data Management and Governance, Chicago Trading Company
Ravi Narang, Head of AI/ML Engineers, Vanguard
Theo Bell, Head of AI Product, Rimes
Mick Hittesdorf, Cloud Product Architect, OneTick

Keynote:

Panel: Deploying AI tools and models into legacy infrastructures and workflows

• Build vs. buy and build and buy AI apps: What are the advantages and limitations of each approach?
• How best can firms evaluate existing third-party tools and platforms in the marketplace?
• Cloud, on premise or hybrid: how do you decide where to host your compute?
• What is the optimal set up for a cost effective and scalable data architecture?
• What are the roles of open source, RAG and vector databases in adding AI to existing infrastructure and workflows?
• What are the concerns about the carbon emissions cost of adopting LLMs and GenAI apps and how can they be approached?
• How should firms concurrently balance the costs, ethical risks and environmental/carbon impacts of AI?

Moderator: Mary Kopczynski, Co-Founder, Women in RegTech New York (WIRTNY)
Ashwin Roongta, Head of Technology: Business Applications, Infrastructure & Services, Wedbush Securities
Bijit Ghosh, Head of Engineering (Cloud & AI/ML), Wells Fargo
Leslie Kanthan PhD.Chief Executive and Co-Founder, TurinTech AI
Charles Lam, Group Director, Head of Collaboration Products, LSEG

Panel: Holistic surveillance: From fragmented signals to unified intelligence

• How are leading firms successfully unifying voice, e-comms, trades, and market data into a single, normalized surveillance dataset—what does true integration look like in practice?
• What are the real-world performance gains or compliance outcomes from deploying fully correlated, multi-source surveillance alerts within a single case-management interface?
• What are the most critical technical and organizational hurdles in implementing an end-to-end surveillance stack—and how are firms overcoming them?
• How do vendors and financial institutions ensure scalability, model transparency, and auditability in AI-enabled surveillance systems?
• As regulators increase scrutiny on conduct and comms monitoring, how are holistic surveillance platforms evolving to meet changing requirements and future-proof compliance operations?

Jon Kroeper, Former Executive Vice President, Surveillance and Investigations, FINRA
Stan Yakoff, RegTech Advisor and Law Professor, Fordham Law School

Panel: Balancing security with performance and innovation in AI

• How can firms integrate encryption, role-based access controls and adversarial-test pipelines “baked-in” to their MLOps workflows, while still meeting low-latency SLAs?
• What end-to-end validations, drift-detection strategies and governance checkpoints are essential to manage model risk—and how can these be automated to keep pace with rapid model iteration?
• In implementing differential privacy or federated learning, what controls and tooling ensure that accuracy and latency remain within acceptable bounds?
• As adversarial attacks, data-poisoning and jailbreaking techniques evolve, what threat-model frameworks and red-teaming practices should security and AI teams jointly adopt?
• Which combinations of model compression (pruning, quantization), hardware acceleration (GPUs/TPUs, FPGAs) and security posture yield the best ROI without compromising compliance?
• How can organizations align to: ISO/IEC 42001 (and 2701/2), the EU AI Act and NIST AI RMF requirements for security, transparency and auditability—while preserving the agility to experiment with new model architectures?
• What proactive discovery and approval workflows can prevent “unsanctioned” models from proliferating, undermining both security controls and performance consistency?

Moderator: Adrian Sharp, Editor, RegTech Insight, A-Team Group
Sateesh Kumar, Head of Digital Transformation Office, Societe Generale
Chandrakant Maheshwari, FVP Lead Model Validator, Flagstar Bank

Hear real world applications of AI and how it is being applied for efficiencies and business value.

Moderator: Julia Bardmesser, CDO Advisor; Adjunct Professor, NYU Stern School of Business

Closing Keynote – Big Tech: The future of AI

• What’s coming next? How will AI continue to shape capital markets?
• What can we learn from other industries?
• The intersection of AI and quantum

Andrew Comas, Industry AI Strategist, Microsoft

Networking Drinks Reception

Ends 

Agenda subject to change