AI in Finance Summit Chicago schedule

•    AI integration in FP&A, reporting, and forecasting
•    Automating repetitive decision processes
•    Live demos of financial copilots

 

 

•    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?

 

 

•    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?

•    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 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?

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.

•    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

•    From Reactive to Proactive: AI-powered analytics to predict and prevent fraud before it happens.
•    Real-Time Defense: Stopping fraudulent transactions instantly while protecting customer experience.
•    Agile & Adaptive Models: Continuously evolving with new fraud schemes and financial crime tactics.
•    Trust as Currency: Strengthening compliance, transparency, and customer confidence through responsible AI

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

•    Early signals of quantum computing integration with AI for portfolio optimization and fraud detection
•    Potential breakthroughs and realistic adoption timelines for finance
•    Managing hype vs. reality: what leaders should invest in today

•    Pre ChatGPT: Natural Language Understanding (NLU) driven architectures and why they were successful
•    ChatGPT: Advent of large language models and their impact on conversational AI design, development, and deployment. Do we abandon the old in pursuit of the new?
•    Post ChatGPT: How do enterprise conversational AI systems evolve to meet existing and future challenges. What are some good design principles to keep in mind while building for the future?

•    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

•    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.

•    How can financial institutions move from AI pilots to enterprise-scale impact?
•    What governance and risk frameworks are needed to scale AI-driven decision-making safely?
•    How can AI help financial institutions stay ahead of increasingly sophisticated fraud?
•    Where will applied AI deliver the greatest competitive advantage in the next three years: risk, fraud, or operations?

•    Real-world applications in advisory, research, and client experience
•    Managing risks: hallucinations, bias, and compliance challenges
•    Separating hype from real ROI

•    What will separate financial institutions that successfully scale AI from those stuck in pilot mode?
•    How can leaders balance innovation with governance when deploying AI in critical financial decisions?
•    Where should organizations prioritize AI investment to drive the greatest financial and operational impact?
•    How will AI reshape the competitive landscape in financial services over the next five years?

•    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