Subsequent to the Bankruptcy Abuse Prevention and Consumer Protection Act Of 2005 (BAPCPA), the “ordinary-course” defense to preferences has become significantly more prevalent for creditors seeking to defend against preference actions. This act modified the requirements for the “ordinary course” defense, making it necessary only to prove that the transfers occurred in the ordinary course within the industry or between the parties, rather than the previously more stringent “and” requirement. The change effectively made the ordinary course preference defense easier and less costly.
Ordinary Course Defense to Preference Claims
In establishing the ordinary-course defense relating to the relationship between parties, the timing and method of transfers between the parties historically and during the preference period is often of primary importance. Many factors may impact the court’s ruling of whether the alleged preferential transfers occurred in the “ordinary course” between the parties. In general, issues commonly considered by courts in determining whether the alleged preferential transfers were ordinary include:
Common Metrics Considered in the Ordinary-Course Defense
Whether the creditor seeks to establish the ordinary course in the industry or between the parties, there are multiple metrics that can attempt to determine if the timing of the transfers in question was “ordinary.” There are certain distinctions that should be considered before making direct comparisons using these metrics or drawing conclusions based on only one of these methodologies.
Payment days. This is the elapsed time from invoice date to payment date. Payment days for transfers made during the preference period are commonly compared to the range of payment days identified for historical transfers.
Accounts receivable days outstanding (otherwise, days sales outstanding or days’ receivables). Days sales outstanding is often calculated for the preference period and compared to the calculated values from the historical period. This metric can be calculated in a numbers of ways, two of which are the a) weighted average method and the b) turnover method as detailed below:
When utilizing metrics such as payment days and days sales outstanding to define ordinary course, it is often necessary to consider several case-specific nuances that may exist in the available data. For example, it may be the case that all invoices did not have the same payment terms, or that some of the transfers related to the contemporaneous exchange of goods. In such cases, a debtor may have taken a significantly longer or shorter time to make certain payments as a result of the corresponding terms and payment arrangements.
Further, while metrics such as those described above may be highly correlated, payments days and days sales outstanding calculated utilizing different methodologies should not generally be directly compared. Rather, these different metrics and methodologies should typically be utilized to give multiple perspectives of the issues at hand.
Utilizing Industry Data to Determine Ordinary Course
Data for a wide range of industries is commonly reported for metrics such as days sales outstanding and days payables outstanding. However, there are certain factors and limitations that should be considered before relying heavily on industry reported metrics, including:
Metrics reported by industry sources may be slightly different than metrics calculated for individual creditor-debtor relationships. For example, when evaluating the transfers in question, payment days for the transfers are often calculated by comparing the date of receipt to the date of the original invoice. However, industry data such as days sales outstanding does not indicate typical time from invoice to payment, but rather the average days outstanding for all receivables in aggregate. While days sales outstanding may be in the 45- to 50-day range for an industry, it is still possible for payment days of individual transfers to commonly be in the 60- to 90-day range, especially if it is common practice for debtors to make payments on oldest outstanding invoices first.
In addition to quantitative considerations, there often are unique qualitative factors and circumstances of the creditor-debtor relationship that must be considered. The parties may have a history of unique payment arrangements that differ from those in the industry and result in seemingly unusual payment practices. Further, if certain economic conditions existed that affected only a particular region or industry subgroup, one should consider such factors prior to making the determination that the transfers in questions were not “ordinary.”
While the ordinary course defense has become a powerful tool, creditors and debtors alike should consider multiple factors and methodologies in attempts to measure the “ordinary course.” The limitations and nuances of available data should be evaluated. Both objective data as well as general industry observations should be considered to help paint a more complete, accurate picture of the ordinary.
1. A preferential transfer in bankruptcy is any “transfer” to or for the benefit of the creditor during the 90-day period counting backwards from the bankruptcy filing date (preference period). A “transfer” is anything of value, tangible or intangible, that the bankrupt customer gave any creditor or gave up for the benefit of a creditor for any reason.
2. Scott Blakeley and Terry Callahan, “In Defense of a Preference,” The Credit Research Foundation, 2004.
3. Standard Industrial Classification (SIC) codes are four digit numerical codes assigned by the U.S. government to business establishments to identify the primary industry of the business. The North American Industry Classification System (NAICS) is a six digit code system developed jointly by the U.S. Economic Classification Policy Committee (ECPC), Statistics Canada, and Mexico's Instituto Nacional de Estadistica y Geografia, and has replaced the SIC system as the standard used by Federal statistical agencies in classifying business establishments for the purpose of collecting, analyzing, and publishing statistical data related to the U.S. business economy.