AI in Finance Summit Chicago schedule

•    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

 

 

•    Enhance risk detection with LLMs that learn from historical investigations and analyst notes.
•    Empower investigation teams with AI copilots for instant insights, narratives, and recommended actions.
•    Automate AML/KYC processes using explainable, auditable AI for transparent compliance.
•    Strengthen defenses by leveraging AI to proactively identify emerging threats and reduce noise?

•    Transitioning from static reports to continuous compliance
•    Empowering regulators and compliance officers with AI copilots
•    Automating AML/KYC through explainable AI

 

 

•    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 

 

•    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, Managing Director, CIBC U.S
Kelly Marie O'Brien, Vice President, Innovation Economy Card, JPMORGANCHASE
Goutham Miryala, Sr. Data Engineer, CHIME

Discover groundbreaking AI technologies aiming to transform finance.

 

•    Beyond Pilots: From experiments to enterprise-wide adoption.
•    Scaling with Trust: Governance, compliance & risk built in.
•    Measurable Impact: Revenue, efficiency & customer value.
•    Future-Proofing Finance: AI ready for shifting regs & markets.

•    How multi-agent systems are driving real-time decision-making in trading, risk, and compliance
•    The role of AI orchestration in creating autonomous financial ecosystems
•    Balancing innovation with human oversight and regulatory trust

• 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?