Federated Learning & Differential Privacy: Architects for Secure AI Collaboration
Wednesday, July 15th, 2026: 10:00 AM to 11:00 AM
In this session, we'll cover the architectural patterns making secure, multi-party AI collaboration a production reality. We'll walk through how federated learning keeps raw data secure inside each institution's boundaries, how differential privacy provides provable guarantees against re-identification, and how these techniques work together with accelerated computing to power real-time AML, fraud, and sanctions workflows.
Virtual
Financial institutions hold some of the richest transaction, customer, and SAR data in the world-as well as the strictest constraints on using it. The result is a structural blind spot in financial crime detection.
The UN estimates less than 1% of laundered funds are ever seized, while compliance teams drown in false positives from models that only see one institution's slice of activity.
FinCEN's 2026 proposed AML/CFT reforms now explicitly reward institutions that use AI to demonstrate program effectiveness, and regulators globally are sharpening expectations around explainability, bias, and data privacy.
What You'll Learn:
- Why siloed AML models miss layered laundering patterns
- How to map federated learning and differential privacy onto existing model risk management controls (SR 11-7)
- What a reference architecture for federated financial crime detection looks like in production, from data residency to audit trail
- How these approaches align with FinCEN's effectiveness framework, the EU AI Act, and FCA explainability expectations
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