Federated Learning & Differential Privacy: Architects for Secure AI Collaboration
Date: 15th July 2026
Time: 10:00 AM ET
Presenters:
• Adrish Sannyasi, Vice President, Customer Solutions and Delivery, Rhino Federated Computing
• Maxim Afanasyev, PhD, FSI Head for AIPAC and Japan, Google Cloud
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.
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.
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
Adrish Sannyasi is Vice President of Customer Engineering and Delivery at Rhino Federated Computing Platform, where he leads teams enabling complex industries such as healthcare and life sciences organizations to operationalize AI - from large language models and protein language modeling to molecular property prediction and clinical applications. His work sits at the intersection of federated computing, cloud data platforms, and applied AI, with a focus on translating complex, multi-institutional collaborations into scalable, measurable outcomes. Adrish holds a BE in Electrical Engineering from VNIT (India), an MBA from the University of Maryland, and a Graduate Certificate in Biomedical Data Science from the Stanford School of Medicine.
Maxim Afanasyev leads Financial Services for Google Cloud in Asia Pacific and Japan, advising C-levels on AI and industry trends. Previously Managing Director at Standard Chartered Bank, he led 200+ AI teams and holds a PhD from Stanford.
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