AI in Finance Summit Chicago schedule

•    Why PE firms have a structural AI advantage that most haven't yet operationalized, and how the constraints of smaller portfolio companies force a more pragmatic, outcome-first approach to AI deployment
•    From raw portfolio data to decision intelligence: building an AI layer across a portfolio without enterprise-scale resources or a dedicated data engineering team
•    Measuring AI ROI in a PE context: how to connect AI investment to hold period value creation, not just internal efficiency

 

 

•    Bridging the gap between data teams and business leadership
•    Enabling scalable AI through cross-functional collaboration
•    Using emotional intelligence to lead change and foster adoption
•    Positioning soft skills as a strategic advantage in AI-driven initiatives

 

 

A single AI agent tackling a complex workflow hits the same bottleneck as a single engineer owning an entire system- it's slow, fragile, and doesn't scale. This talk makes the case for multi-agent architectures where specialized agents run in parallel, each owning a discrete subtask, coordinated by an orchestration layer. We'll cover how to decompose workflows into parallelizable units, manage inter-agent communication and dependencies, and handle failures gracefully.

•    What are the most effective strategies to scale AI initiatives while keeping costs under control?
•    How can organizations balance the transformative potential of generative AI with the risks it introduces?
•    In what ways AI Agents are reshaping business operations, and what challenges come with their adoption?
•    How do you measure and ensure tangible business value from AI investments across the enterprise?

Panelists:
Hunter Treschl, Manager, AI Center of Excellence, CBOE GLOBAL MARKETS
Karthikcharan Suresh, Sr. Lead Data & AI, HIGHSTREET INSURANCE PARTNERS
Brij Mohan, Vice President-Software Development, LPL FINANCIAL 
Kishan Kumar, Senior Software Engineer - Data and AI, CHIME
Kelly Marie O'Brien, Vice President, Innovation Economy Card, JPMORGANCHASE


 

•    Institutional AI Challenges: Although large language models (LLMs) can generate market insights efficiently, issues such as hallucinations and unverifiable statements render them unsuitable for regulated production environments.
•    Architectural Innovation: Harbor Capital Advisors has developed a Multi-Agent Act-Verify-Refine (AVR) framework—a self-correcting, validation-focused system designed to surpass traditional one-shot generation methods, achieving institutional-grade reliability.
•    Self-Correcting Mechanism: Dedicated agents systematically dissect narratives into individual claims, validate each assertion, and refine results iteratively until regulatory requirements are fully satisfied.
•    Key Recommendations: Establish agentic state machines centered on validation, incorporate automated claim auditing, ensure robust data grounding, and delineate specific Human-in-the-Loop processes to maintain subject matter expertise while ensuring scalability.

 

•    How can financial institutions ensure the quality, integrity, and governance of customer data to drive reliable AI outcomes?
•    In what ways does trusted customer data accelerate the development of innovative, personalized financial products and services?
•    What are the biggest challenges in balancing customer privacy with the need for data to fuel AI innovation?

Panelists:
Alan Zhang, SVP Credit Analytics & Infrastructure, FIRST CITIZENS BANK
Goutham Miryala, Sr. Data Engineer, CHIME
Will Johnson, Head of Data Engineering and Development, CERITY PARTNERS
Anjan Bhattacharya, Director - Cleared Derivatives and Collateral Technology, BANK OF AMERICA 
Jubinder Singh, Senior Manager, Corporate Strategy AI, CHARLES SCHWAB

Discover groundbreaking AI technologies aiming to transform finance.

 

•    Bridging the gap between data teams and business leadership
•    Enabling scalable AI through cross-functional collaboration
•    Using emotional intelligence to lead change and foster adoption
•    Positioning soft skills as a strategic advantage in AI-driven initiatives

• How AI is evolving from prediction to execution (AI agents in trading, risk, and operations)
• What “agentic AI” means for banks, hedge funds, and fintechs
• The future role of humans in AI-driven financial decisions

• Transforming MDM with autonomous AI agents 
• Improving data quality and governance through intelligent automation 
• Enabling trusted, scalable AI with unified enterprise data 

•    Agentic AI driving smarter trading decisions.
•    Multi-agent systems for better forecasting and risk.
•    Quantum computing reshaping compliance.

•    From Pilots to Scalable Impact: How financial institutions can evolve AI maturity to unlock measurable business outcomes.
•    The Growth Catalyst: Why AI maturity is the key to driving efficiency, innovation, and revenue generation in financial services.
•    Building Trust at Scale: How to align governance, compliance, and customer trust as AI capabilities mature.

•    Why today’s semantic layer is breaking and silently corrupting both analytics and AI
•    How we built a deterministic, versioned semantic control plane (no LLM guesswork)
•    Turning semantics into infrastructure that powers both BI and GenAI consistently

•    How does poor data quality directly undermine AI outcomes in finance?
•    What governance practices truly build trust in AI-driven decisions?
•    How can institutions balance innovation with strict data controls?
•    What lessons from early adopters show the ROI of strong data governance?