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A Method for Improving the Benchmarks Used to Monitor ACH Returns

Published March 1, 2010

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Richard M. Todd Former Vice President and Advisor to the CICD
Ron J. Feldman Senior Vice President
Mark Lueck Economist
Olivier Armantier Research Officer, Federal Reserve Bank of New York
Michele Braun Officer, Payments Policy, Federal Reserve Bank of New York
Dennis Kuo Economist, Federal Reserve Bank of New York
A Method for Improving the Benchmarks Used to Monitor ACH Returns


Close to 2 percent of consumer debits processed in the automated clearinghouse (ACH) payments system are returned to the financial institution that submitted the transaction, for reasons such as insufficient funds, incorrect account information, or lack of authorization (as reported by the consumer). A returned debit transaction can lead to loss at the financial institution that originated the debit, as when the institution has given its customers use of the funds sought in the debit but then is unable to obtain repayment from the customer. Concerns about financial institutions’ ACH return-item risks have grown over the past decade, as the volume of ACH transactions has grown rapidly and expanded into relatively anonymous and one-time types of transactions thought to be more vulnerable to fraud than the more traditional ACH transactions like prearranged, ongoing consumer bill payments. We show that the management of return-item risks associated with ACH consumer debits may be improved by analysis of return-rate distributions, such as the distribution of return rates across ACH originators for all consumer debits as well as distributions conditioned on a specific type of consumer debit forward transaction. Examples of these types of distributions, computed from a broad sample of ACH data, have not been published before, to our knowledge. We tabulate several such distributions, using data on all consumer debit forward and return items processed by the dominant U.S. ACH operator (Federal Reserve Automated Clearinghouse, or FedACH) during a three- (forwards) to six- (returns) month period in 2006 and an algorithm to match about 90 percent of returns to their corresponding forward items. Our matched data show that the distribution of return rates across originators is highly skewed (a distinct minority of originators account for the majority of returns), is not strongly related to the volume of originations or the deposit size of the originating institution, and varies depending on the type of forward transaction, with the distributions of telephone- and web-initiated returns different both from each other and from the overall distribution of returns in ways that may have implications for risk managers. Insufficient funds are the dominant reason items are returned, but in the cases of telephone- and web-originated transactions, some originators are more successful at avoiding this type of return than their peers. These findings, which only illustrate the types of analysis that can be done by using our methods, imply that the limited ACH return-risk benchmarks currently in use, which are mostly simple return-rate averages at high levels of aggregation, are not sufficiently detailed to support optimally effective ACH return-rate monitoring and risk control.