AI in Finance Summit New York schedule

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

 

 

Can we open the black box of large language models and make AI in finance truly transparent? As financial institutions adopt LLMs for tasks like trading, compliance, and advisory, the need for transparency is more critical than ever. Understanding how these models internally reason about financial topics is key to ensuring trust and regulatory alignment. This talk covers how mechanistic interpretability can reveal internal patterns in LLMs related and use it for applications such as trading, sentiment analysis and hallucination reduction.

•    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?

•    Using AI for secure, real-time global transactions
•    Overcoming fraud and data challenges in AI-powered payments 
•    The role of AI in CBDCs and next-gen payment rails 

 

•    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

•    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

•    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

•    Thriving in uncertainty, AI built to perform under market volatility and rapid change.
•    Trust by design, resilience rooted in transparency, governance, and ethical AI.
•    Future-ready finance, adaptable AI that meets new risks, regulations, and customer demands

•    Failures First – What early missteps revealed about AI’s real limits in finance.
•    Scaling Wins – How pioneers turned pilots into enterprise-wide impact.
•    Trust Factor – Building governance and transparency from the start.
•    Next Moves – What early adopters see as the boldest opportunities ahead.

•    Predicting systemic risks before they escalate
•    Partnering with central banks for resilient oversight
•    Designing human-centered, future-proof ecosystems

•    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 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?

•    Beyond automation: how AI agents move from task execution to strategic decision-making.
•    Efficiency unlocked: streamlining operations and reducing costs across the financial ecosystem.
•    Revenue engine: transforming AI agents into drivers of growth, new products, and customer value.
•    The future of finance: AI agents reshaping roles, talent, and business models.

 

 

•    How can financial institutions strike the right balance between rapid AI innovation and strict compliance requirements?
•    What are the most common security risks when deploying AI in finance, and how can organizations mitigate them?
•    How can firms ensure that AI tools are not only compliant at launch but remain compliant as regulations evolve?
•    What does it take to scale AI solutions across an enterprise without compromising governance or customer trust?
•    Looking ahead, what capabilities will define the next generation of AI tools that are truly enterprise-ready for finance

•    Empowering people, not replacing them: building AI teams that thrive in regulated financial environments.
•    Trust at every step: embedding human-in-the-loop strategies to ensure responsible governance.
•    Culture as the catalyst: driving organization-wide adoption of AI through mindset and leadership transformation.

Discover groundbreaking AI technologies aiming to transform finance

•    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?
•    How can organizations build and maintain customer trust while scaling AI solutions that rely heavily on sensitive data?

•    Collaborative Frameworks: Regulators and institutions shaping AI guardrails together.
•    Trust by Design: Embedding transparency, ethics, and accountability into every AI solution.
•    Compliance as Innovation: Leveraging regulation to unlock new opportunities and customer value.
•    Future-Proof Finance: Ensuring AI systems adapt to shifting rules, risks, and market dynamics

•    Domain Accuracy: Assess how LLMs perform on finance-specific reasoning tasks  
•    Safety & Compliance: Measure domain-specific safety, ethics, and regulatory alignment 
•    Cost vs Capability Trade-offs: Compare performance, inference cost, latency, and scalability in live financial use
•    Agentic & Tool-Enabled Behavior: Go beyond single-response metrics: evaluate reasoning chains, tool use, memory, and multi-step workflows

•    Clarity in Complexity: Why explainability is essential for financial institutions adopting AI at scale.
•    Trust & Compliance: Meeting regulatory requirements and strengthening customer confidence through transparent AI.
•    From Black Box to Glass Box: Practical approaches to make AI decisions understandable and auditable in finance.
•    Responsible Innovation: Balancing advanced AI capabilities with accountability, fairness, and ethical considerations



 

•    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.