That's a wrap on Rev New York
Ten sessions. Fourteen speakers. One clear signal: the organizations furthest ahead in enterprise AI didn't add governance after the fact. They built it from the start.
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Three themes defined New York
Regulation isn't the obstacle - it's the map
The model era is giving way to the app era
The operating model is the real challenge
What was covered
The guardrails built for predictive models were already straining under GenAI—now agentic systems are breaking them entirely. Discover the AI risk blind spots financial institutions can't afford to ignore, and how to move beyond principles toward eliminating real ethical, reputational, and regulatory exposure.
Virtue Consultants
Enterprises have the models, the talent, and the budgets. What's missing is the operational foundation to turn AI investment into measurable business impact. Discover how a full enterprise AI application platform empowers teams to build, govern, and scale AI-powered decisioning systems.
The Federal Reserve's David Palmer, architect of SR 11-7, will share how banks are deploying AI, key supervisory considerations, and what regulators look for in governance and guardrails. Along with Domino COO Thomas Robinson, he'll unpack the new SR 26-2, discuss what genAI means (and doesn't) for model risk, validation, and the broader model development lifecycle.
Federal Reserve Board
73% of FSI leaders report underwhelming AI ROI - despite leading every industry in adoption. Chun Schiros, Field CTO at AWS, explains why, maps a three-phase path from pilot to transformation, and reminds us what hasn't changed in 100 years: the mission of serving humans in moments of uncertainty.
Get an inside look at data science at the Federal Reserve Bank of NY and hear how LLMs can be used to measure proxies relevant to research, such as sentiment and Federal Reserve transparency. Mark Vandergon covers key research developments including lessons from a recent paper bridging data science with economic frameworks through cross-functional collaboration.
The long-awaited MRM guidance is here. But gen AI and agentic AI are explicitly out of scope. What does it mean when you’re scrambling to govern a rapidly-evolving AI landscape? Hear directly from senior MRM leaders at TIAA, Capital One, and an advisor who helped shape the original SR 11-7, on how the new guidance changes their approach, and what enterprise risk leaders should do now.
Rodanthy Tzani | Sphaleron Founder & Risk and Compliance Advisor | Former Head of MRM at NY Life | Sphaleron
Arthur Robb | Head of MRM | TIAA
Without standard modeling practices, technical debt accumulates fast. Learn how Fannie Mae built a structured, reproducible workflow orchestrator that brings trust, transparency, and governance to every stage of the model development process.
Rabbani Mozahid | Director | Fannie Mae
How does a small data science team drive outsized impact in a global media company? At Vevo, we use AI and automation to scale our influence across the business, up-leveling our company capabilities while navigating governance and adoption challenges.
Vibe coding lowered the barrier; agentic engineering raises the stakes. As AI takes on full dev lifecycles, the challenge isn't autonomy—it's knowing when humans must stay in the loop. Learn to design HITL checkpoints, build governance, and scale toward dynamic virtual engineering teams.
Regulated enterprises don't have a model problem—they have an application problem. See live examples of AI applications driving real business outcomes, not just proof-of-concepts, and learn how enterprises can build and deploy production-ready AI apps in hours, not weeks.
Danny Stout | Director of Product Marketing | Domino
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