Average Lateness Methodology

Two recent decisions from the United States Bankruptcy Court for the Southern District of New York, affirmed the use of "average lateness" methodology to examine both the subjective and the objective components of the ordinary course of business defense to preference actions. This article explains the those decisions.

15 minute read February 28, 2015 at 11:00 PM
By
Edward E. Neiger and Marianna Udem
Average Lateness Methodology

Two recent decisions from the United States Bankruptcy Court for the Southern District of New York, Quebecor World Litigation Trust v. Clarklift-West, Inc. dba Clarklift Team Power (In re Quebecor World (USA), Inc.), 2014 WL 5292981 (Bankr.

This premium content is locked for LawJournalNewsletters subscribers only

ENJOY UNLIMITED ACCESS TO THE SINGLE SOURCE OF OBJECTIVE LEGAL ANALYSIS, PRACTICAL INSIGHTS, AND NEWS IN LawJournalNewsletters

  • Stay current on the latest information, rulings, regulations, and trends
  • Includes practical, must-have information on copyrights, royalties, AI, and more
  • Tap into expert guidance from top entertainment lawyers and experts

Already have an account? Sign In Now

For enterprise-wide or corporate access, please contact Customer Service at [email protected] or call 1-877-256-2473.

NOT FOR REPRINT

© 2026 ALM Global, LLC, All Rights Reserved. Request academic re-use from www.copyright.com. All other uses, submit a request to [email protected]. For more information visit Asset & Logo Licensing.

Continue Reading

Most firms are aiming their newest tools at the work they already do — pouring their most powerful technology into running the same tasks a little faster. But when everyone automates the same tasks at once, no one pulls ahead. That reaches the future a little faster while leaving a firm’s largest opportunity untouched — and that opportunity isn’t doing more of the existing work, but transforming how the high-value work gets done.

June 01, 2026

Artificial intelligence is rapidly embedding itself into legal workflows, but much of the conversation treats all use cases as if they carry the same level of risk, even if they do not. The more useful question is not whether AI works, but where it can be safely applied and where it cannot.

June 01, 2026