AI in Finance Summit Fall schedule

Nonlinear AI/ML representations, such as Random Fourier Features, can reshape financial data in powerful ways, but added complexity does not automatically improve forecasting performance.
What matters is not simply how many features or parameters a model has, but how much stable forecasting flexibility the data used can support, after accounting for sample size, feature geometry, and regularization.
In noisy, low-signal financial settings, disciplined benchmarks—regularized linear models and exact kernel methods—remain critical for determining whether model complexity is adding genuine value.

•    Autonomous agents need guardrails—or they create new risks 
•    Governance must move at the speed of AI
•    Trust in finance now depends on controlling algorithms 
•    No oversight, no scale: governance is the real enabler

•    No explainability, no trust; black boxes will not scale in finance
•    Accountability does not disappear; AI shifts who owns the risk
•    Opaque models create invisible systemic risks
•    Trust is the real currency; AI can erode or amplify

•    Memory turns AI from reactive to strategic
•    Reasoning is the edge—prediction alone is not enough
•    Smarter agents make higher-stakes financial decisions
•    The real challenge: trusted memory, controlled reasoning

•    Where is AI actually making money, not just generating headlines?
•    How much of your AI spend is real ROI versus expensive experimentation?
•    What is the most overhyped AI use case in finance right now?
•    Why do so many AI pilots look promising but fail to deliver real value at scale?

•    How AI adoption in financial services evolved from experimentation in 2024 to enterprise-wide implementation in 2026
•    Key lessons learned from scaling Generative AI initiatives across compliance, risk, operations, and customer experience
•    What financial institutions must prioritize next to move from AI efficiency gains to long-term competitive advantage

•    Is generative AI already becoming commoditized in finance?
•    If everyone has copilots, where is the real competitive edge?
•    What comes after generation; are you ready for AI that actually makes decisions?
•    Who will lead the next wave; incumbents reinventing themselves or AI native players

Panelists:
Alaa Moussawi, Chief Data Scientist, NEW YORK CITY COUNCIL

•    Data sharing is no longer required to collaborate; the model moves, not the data
•    Privacy preserving techniques are turning compliance into a competitive advantage
•    Synthetic data is expanding what is possible without exposing sensitive information
•    The future of financial AI will be built on collaboration without compromising trust

High impact sessions where leading companies showcase real AI use cases in finance, demonstrating how their solutions drive tangible results and business value in practice

High impact sessions where leading companies showcase real AI use cases in finance, demonstrating how their solutions drive tangible results and business value in practice

A fully interactive, no-slides session where attendees can engage directly with industry experts, ask candid questions on AI in finance, and receive practical, unfiltered insights in real time

Networking & Deal Flow Sessions Topic Pods: Algorithmic Trading | AI Risk Models | RegTech Tools | GenAI in Banking | Quant Research | AI Ethics | Climate Risk AI |

 (5-min rotations, 40 min total)  

•    Most AI pilots look impressive, but fail when exposed to real financial data and risk conditions
•    Accuracy in finance is not optional; small errors can lead to significant financial and regulatory consequences
•    The real challenge is moving from controlled demos to robust, reliable systems that perform at scale

•    Adapt faster or fall behind
•    Static strategies are already obsolete
•    AI advantage goes to the most agile
•    Continuous change is the new business model

•    How much of your governance is automated versus still relying on manual oversight?
•    Are you building governance for today’s models or for autonomous systems you cannot fully control yet?
•    If governance fails at scale, is it a technology gap or a leadership blind spot?

Panelists: 
Chandni Bhatiam, Vice President, Lead Quantitative Development, J.P. MORGAN
Sonia Bhargava, Vice President - Software Engineer, BANK OF AMERICA
Krishna Chaitanya Yarlagadda, Director- Data Science & AI at MERCURYFINANCIAL

•    The real challenge is not automation, it is knowing when humans must stay in control
•    Poor handoffs create risk; the right balance between AI and human judgment creates trust
•    Human oversight is shifting from decision making to exception handling and validation
•    The competitive edge is designing workflows where humans and AI amplify each other

•    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

•    Are traditional stress tests already obsolete in a world of real time AI driven risk?
•    If AI can simulate infinite scenarios, are we finally solving risk or just creating new blind spots?
•    How much real time risk visibility do institutions actually have versus what they claim?
•    When AI reacts faster than humans, who is really in control of risk decisions?

Panelists:
Cristian Homescu, Director, Portfolio Analytics, Chief Investment Office, Global Wealth and Investment Management (GWIM), BANK OF AMERICA MERRILL LYNCH

•    Predicting needs is no longer enough; the real edge is acting before intent is expressed
•    Behavioral AI turns data into anticipation, not just personalization
•    The winners will move from reactive offers to proactive, seamless experiences
•    The risk is not over targeting; it is getting it wrong and breaking trust

 

•    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

 

•    What does agentic AI actually look like in production, and how far are you from it?
•    Where is agentic AI truly outperforming traditional analytics, and where is it still falling short?
•    What broke first when you tried to deploy agentic systems; your tech, your data, or your culture?
•    How much autonomy are you really willing to give AI when real money and risk are on the line?
•    What will it take for you to trust AI to make decisions, not just recommendations?

 

•    Chatbots assist, autonomous entities decide and execute across credit and treasury
•    Underwriting is shifting from static models to continuous, real time intelligence
•    Treasury is moving from manual control to algorithmic execution at speed and scale
•    The real challenge is not capability, it is trust, control, and accountability

 

A fully interactive, no-slides session where attendees can engage directly with industry experts, ask candid questions on AI in finance, and receive practical, unfiltered insights in real time

 

•    How far are you willing to trade accuracy for fairness in credit models—and where do you draw the line?
•    Are most “bias mitigation” efforts actually working, or just ticking a compliance box?
•    When an algorithm gets it wrong, who is truly accountable—the model, the team, or the business?
•    Is explainability a real priority, or does it get sacrificed the moment performance is at stake?
•    Will regulation drive meaningful ethical AI—or just slow innovation without solving the core issues?

Panelists:
Jaydip Mukhopadhyay, Vice President, Data Science and Model Risk, AMERICAN EXPRESS

 

•    AI first architecture is not an upgrade, it is a complete rebuild from the ground up
•    Legacy culture slows AI, AI native culture scales it
•    Business models are shifting from products to intelligent, data driven services
•    The gap will not be incremental, it will be exponential between AI native and legacy institutions

 

•    Is quantum AI in finance a real near term opportunity or just a long term research bet?
•    What specific financial use cases could quantum AI realistically unlock that classical AI cannot?
•    Are institutions investing in quantum AI for strategic advantage or simply to avoid being left behind?
•    What is the biggest misconception leaders have today about quantum AI in finance?

 

 

•    Hybrid quantum AI approaches are moving from theory to early signals in live trading
•    The edge is emerging in complex optimization problems classical methods struggle to solve
•    Real value is not outperforming every trade, it is improving portfolio level efficiency
•    The question is no longer if it works, but where it delivers measurable advantage

 

•    How to overcome the challenges of aligning business and engineering on the AI journey
•    Unlocking the power of process optimization to drive sustainable profitability
•    Strategies for building a bridge between process and delivery in ai finance
•    Best practices for implementing ai solutions that deliver real-world results 

 

• Risk is no longer simulated periodically; it is monitored and adapted in real time
• Generative AI enables dynamic scenario creation, not just predefined stress tests
• The shift is from backward looking risk models to forward looking intelligence
• The real edge is not predicting risk, it is responding to it instantly