How agentic systems turn financial intent into

auditable action—beyond prompts, beyond models.

6:00 PM Seminar Begins

7:30 PM Reception

Hybrid Event

Fordham University

McNally Amphitheater

140 West 62nd Street

New York, NY 10023

Free Registration!

For Virtual Attendees: Please select virtual instead of member type upon registration.

Abstract:

Agentic AI in finance is shifting from conversational assistance to systems that execute actions across data, models, and workflows. This evolution raises a central challenge: how to build AI that is not only capable, but trustworthy in high-stakes, regulated environments.

This talk presents a practical framework for building trustworthy agentic AI for finance, structured around four building blocks: knowledge discovery on unstructured data, knowledge discovery on structured data, multi-agent reasoning and orchestration, and governance with continuous interpretability and surveillance. We argue that trust is a system-level property, emerging from how these components interact rather than from model accuracy alone.

As a focused deep dive, we examine Text-to-SQL as a stress test for trustworthy execution, highlighting challenges such as schema grounding, semantic precision, and material correctness. Using recent financial benchmarks as reference, we show how agent orchestration and governance layers transform promising capabilities into production-ready financial systems.

Bio:

Stefano Pasquali is Senior Vice President and Head of Financial Services Solutions at Domyn, where he leads the creation of sovereign, explainable, and fully auditable AI for mission-critical financial applications. His work combines agentic AI architectures, knowledge-graph reasoning, proprietary financial models, and large-scale LLMs, enabling institutions to own, govern, and scale AI with full compliance and IP protection.

Previously Managing Director and Head of Investment AI at BlackRock, he advanced AI, machine learning, and GenAI to support investors and the Aladdin platform. Earlier he directed Liquidity Research at Bloomberg, where he contributed to BVAL and created Liquidity Assessment (LQA). A theoretical and computational physicist, he has taught at Rutgers and Columbia University and remains active in global conferences and regulatory dialogues on responsible AI and model governance.