The knowledge platform for the financial technology industry
26 June, 2025

Countdown

Location

@Ease, 7th Floor, 605 Third Avenue, New York

Agenda

08:15am

Registration and Sponsor Networking

09:00am

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

09:05am

Practitioner Innovation Keynote: The evolution of AI and how to generate value from your investment

  • What are the current trends in AI and where can Generative AI deliver results over traditional AI?
  • How should firms balance the opportunities with the risks to get maximum value from AI?
  • How should firms plan for workforce transformation and engagement?
  • What are the key requirements (people, process, technology) for successful AI
  • What role will agents play and how can they add value?
  • How should firms measure AI’s impact, short term vs long term gains; what are the metrics to measure success?
09:35am

Panel: From efficiency to value – 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?
10:20am

Keynote: Scaling up and delivering value with GenAI – Real world case study

10:40am

Morning Break and Sponsor Networking

11:10am

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?
11:55am

Case study: Extracting structured data from unstructured data

12:15pm

Panel: Deploying AI tools and models into existing 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? 
1:00pm

Lunch and Sponsor Networking

2:00pm

Keynote: A Regulator’s perspective on AI

  • How does the current regulatory framework support AI and is any further clarification needed?
  • How is the Regulator continuing to invest in data and technology to support digital markets and responsible AI?
  • What are the regulatory expectations around data quality and data lineage for AI and how should firms prepare?
  • What are the priorities and key messages for firms as they develop AI compliance strategies?
2:20pm

Panel: Best practices for model risk management, security and performance

  • What are the challenges in testing models and what are the tools and practices that can enhance efficiency and effectiveness of testing?
  • What are the challenges and solutions for managing model performance over time?
  • How do you ensure the accuracy and reliability of models?  What are the strategies to manage hallucinations, ethics and bias?
  • How is the threat landscape for LLMs evolving and how should firms manage model security, shadow AI and threats from jailbreaking?
  • Models are costly to develop and maintain, how can costs be reduced by optimizing model performance?

 

3:00pm

Keynote: The opportunities and challenges of autonomous agents in capital markets 

3:20pm

Afternoon Break and Networking with Sponsors

3:55pm

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

Front Office and Trading Technology

  • Use Case Drill Down: How AI can improve transaction and communications surveillance outcomes
  • Use Case Drill Down: Using AI to improve trade matching rates and reduce reconciliations
  • Use Case Drill Down: Using AI in the data discovery process for deal making and portfolio management
  • Use Case Drill Down: Leveraging AI to deliver data-driven market intelligence and insights for the buyside
  • Use Case Drill Down: Using AI to scour descriptive information and source liquidity in OTC markets

 

Mid/Back Office and Data Management

  • Use Case Drill Down: Applying governance and compliance principles to AI deployments
  • Use Case Drill Down: FinCrime – KYC, AML, client onboarding and screening
  • Use Case Drill Down: How to leverage AI to improve the quality and production readiness of your data
  • Use Case Drill Down: AI in the ESG due diligence process
  • Use Case Drill Down: Using AI for collection and validation of regulatory reporting data
  • Use Case Drill Down: The role of AI in market and operational risk management
4:55pm

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
5:25pm

Networking Drinks Reception

6:25pm

Ends 

Agenda subject to change 

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