Session Presentations
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Day 1
Keynote Presentation: Beyond Algorithms: Agentic AI and the Quantum Reckoning in Financial Services
- Why the quantum threat is a migration deadline, not a broken algorithm — and why "harvest now, decrypt later" makes it a present-day risk.
- How agentic AI and quantum are converging on the same cryptographic trust fabric.
- The uniquely irreversible exposure facing digital assets, custody, and tokenized real-world assets.
- What financial institutions should do now — inventory, crypto-agility, and agent identity — before the hardware arrives
Ellis Wong, Chief Information Security Officer, JST CAPITAL
Keynote Presentation: The Human Glue in AI: How Soft Skills Drive Impact in Financial Services
- 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
Lovedeep Saini, Chief Analytics Officer, CONNER STRONG & BUCKELEW
Keynote Presentation: Ahead on Ambition, Behind on Production: Closing Financial Services' Agentic AI Gap
Most organizations expect to move the majority of their AI experiments into production within months — but only a quarter have actually gotten there. Financial services isn't immune to that gap; if anything, the institutions in this room are investing hardest into closing it. In this keynote, Lance Senoyuit, Sr. Principal, Financial Services Industry, Executive Advisor, SAP America’s breaks down why the gap between AI ambition and AI in production persists, where the value quietly leaks out along the way, and what it actually takes to turn deployed AI into AI that changes the business.
Key Talking Points:
- The gap nobody's closing fast enough — more than half of organizations expect to move most of their AI experiments into production within months. Only a quarter have actually gotten there. That gap is where most institutions are sitting right now.
- Where the value actually leaks out — it's rarely the model. Fragmented data, systems that don't talk to each other, and agents bolted onto old workflows instead of built into them quietly cap what "deployed" AI can deliver.
- What closing the gap looks like — how leading institutions are turning proprietary data into a strategic asset and building the platform foundation that lets agents operate on real business context, not isolated pilots.
Lance Michael Senoyuit, Financial Services Industry, Executive Advisor, SAP AMERICAS REGION
USE CASE SHOWCASE: Customer-Facing AI: Making Agent Outputs Trustworthy Enough to Ship
- Why agents fail in front of customers. Most customer-facing AI breaks down not because of the model, but because of the data and context behind it: stale inputs, inconsistent definitions, and answers that can't be traced back to a source.
- Building traceability into every output. How to ground agent responses in governed, versioned data so every answer can be reproduced, audited, and explained, which is critical in regulated financial environments.
- A practical path from pilot to production. The checks, guardrails, and evaluation practices that give compliance, risk, and business teams the confidence to sign off on putting an agent in front of real customers.
Luke Pendergrass, Founding Product, Xorq Labs
Keynote Presentation: Building Financial AI Agents with Memory & Reasoning
- 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
Anant Natekar, Senior Director Software Engineering, NORTHWESTERN MUTUAL
Dhagash Mehta, Head of Applied Machine Learning Research for Investment Management, BLACKROCK MANAGEMENT
Keynote Presentation: AI in Finance: A Two-Year Retrospective (2024–2026)
- 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
Todd Lyon, VP Engineering Manager, TABBANK
USE CASE SHOWCASE: Measuring impact of Agents in Production using Honeycomb
- Latency and errors directly impact critical customer flows, and AI Agents are making those flows more complex
- Honeycomb lets you measure the impact of changes and problems in production at an aggregate and detail level using application traces and logs
- As a result, you know where agents and AI-deployed code improved, degraded, or broke functionality fast, often before your users notice.
Ken Rimple, Senior Developer Relations Advocate, HONEYCOMB
USE CASE SHOWCASE: The Missing Half: Why AI Needs Quantum to Solve Finance Problems
- AI struggles with searching combinatorial, highly correlated problems but excels at representing them in a smaller, simpler space.
- Quantum processors excel at search but have few noisy qubits and require the problem to be prepared in a specific form.
- Together they cover each other’s gaps, demonstrated using two finance cases: portfolio optimization and fraud detection.
David Dentelski, Chief Quantum Scientist, Qlerate
Day 2
Keynote Presentation: Agentic AI Explainability in Finance
- Building trust and transparency in agentic AI decision-making within financial services
- Balancing explainability, governance, and regulatory expectations for autonomous AI systems
- Practical approaches to making agentic AI outputs understandable and actionable for stakeholders
Hariom Tatsat, Director, AI Quant, BARCLAYS
Keynote Presentation: Observability and Attribution in Large Scale Multi-Agent Systems
- Scaling AI systems reliably in production
- Monitoring and troubleshooting multi-agent systems
- Best practices and open challenges for production
Manisha Verma, Executive Director in AI/ML, JPMORGANCHASE
Keynote Presentation: Digital Transformation Simplified Via AI for a Mission-Critical System
- How AI can simplify the modernization of complex, mission-critical systems while maintaining reliability and business continuity.
- Moving beyond traditional transformation approaches by using AI to accelerate decision-making, automation, and operational efficiency.
- Key lessons for implementing AI at scale while managing risk, governance, and integration with existing systems.
Ajay Upadhyay, Director, Software Engineering, MASTERCARD
Keynote Presentation: Governing AI Agents in Finance: Tactics, Strategies, and Tools for a New Era
- 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
Hari Kishan, Director, Cloud Engineering, MANULIFE