Webinar recording
Published 27 Jan 2026

US Treasury Central Clearing with BNY, Broadridge, DTCC, and SIFMA

What will determine whether firms reach mandatory US Treasury central clearing on time?

US Treasury Central Clearing with BNY, Broadridge, DTCC,  and SIFMA

In Partnership with

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Speakers

Brian Disken

Brian Disken

Executive Director, Head of Treasury Product

DTCC
Mark Pekala

Mark Pekala

VP, Fixed Income Product Management

Broadridge
Martin Lawrence

Martin Lawrence

Chief Customer Officer

VX
Nathan Wuerffel

Nathan Wuerffel

Global Head of Market Structure

BNY
Stephen Byron

Stephen Byron

Managing Director, Head of Technology, Operations and Business Continuity

Sifma

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Mandatory US Treasury central clearing is no longer only a planning exercise. Firms now need to move from assessment to execution while the operational, capital and documentation implications become more immediate. 

What will separate firms that are ready from those that fall behind as the deadline approaches? How quickly can market participants move from high-level analysis to practical decisions on scope, clearing models and implementation dependencies?

This session brings together Brian Disken of Depository Trust & Clearing Corporation (DTCC), Mark Pekala of Broadridge, Nate Wuerffel of BNY Mellon, Steve Byron of Securities Industry and Financial Markets Association (SIFMA) and Martin Lawrence of The ValueExchange to examine how firms are progressing from readiness assessment towards live implementation across US Treasury central clearing.

The session, produced in collaboration with SIFMA, BNY Mellon, Broadridge and DTCC, highlights:

  • Why time is becoming the main constraint as firms remain in assessment mode

  • How identifying in-scope Treasury activity is central to reliable implementation planning

  • What clearing model choices mean for capital usage, margin treatment and operations

  • How service provider, documentation and testing dependencies are becoming more visible

  • Why some firms see value in moving earlier and refining their models after go-live

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