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Federal Reserve Bank of Minneapolis

FINANCIAL POLICY WORKING PAPER
A Method for Improving the Benchmarks Used
to Monitor ACH Returns
OLIVIER ARMANTIER
Research Officer
Federal Reserve Bank of New York
MICHELE BRAUN
Officer, Payments Policy
Federal Reserve Bank of New York
RON J. FELDMAN
Senior Vice President
Federal Reserve Bank of Minneapolis
DENNIS KUO
Economist
Federal Reserve Bank of New York
MARK I. LUECK
Senior Economist
Federal Reserve Bank of Minneapolis
RICHARD M. TODD
Vice President
Federal Reserve Bank of Minneapolis

March 2010

Published by the Division of Supervision, Regulation, and Credit
Federal Reserve Bank of Minneapolis
The views expressed here are those of the authors and not necessarily those of the Federal Reserve Bank of Minneapolis or
the Federal Reserve System.

This page is intentionally left blank.

A Method for Improving the Benchmarks Used to Monitor ACH Returns
By Olivier Armantier, Michele Braun, Ron J. Feldman, Dennis Kuo, Mark I. Lueck,
and Richard M. Todd

Abstract: 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.

Introduction

The ACH in the United States is an electronic network by which depository financial institutions
(banks, for simplicity) transmit and settle batches of certain types of payments in which a
3

customer of the originating depository financial institution (ODFI) wishes to credit (pay) or debit
(be paid by) a customer of the receiving depository financial institution (RDFI). The vast
majority of ACH transactions are transmitted and settled successfully, but a small fraction (under
2 percent for the consumer debit transactions we study) are returned, either unpaid or for a
refund, to the ODFI by the RDFI. Typical reasons include insufficient funds in the receiving
customer’s account (for debit transactions), an incorrect account number, or the receiving
customer’s assertion that he or she had not authorized the debit. The return of a debit item can
result in loss for the ODFI, if it has already credited its customer with the payment and that
customer cannot be debited to return the funds, such as when the customer’s account balance is
too low or the account has been closed.

Traditionally, most ACH payments involved large batches of small transactions submitted by an
established and familiar bank customer on an ongoing basis and with the verified approval of the
customers involved. Examples include regular payroll deposits, in which an employer’s bank
processes credits to the accounts that employees maintain at other banks; monthly Social
Security deposits to recipients’ accounts; and ongoing utility bill payments, in which customers
allow the utility company’s bank to send a debit to be collected from the customer’s account at
the RDFI. 1 In recent years, however, much of the double-digit rate of growth in the ACH system
has been driven by new types of one-time, as opposed to recurring, debit transactions, which
inherently allow less time to confirm an affected customer’s identity, account information, and
consent. Examples include transactions initiated by conversion of a paper check to an ACH
transaction at the point of sale and one-time payments initiated by telephone (TEL transactions)
or over the Internet (WEB transactions).

These newer ACH services elevated the level of ACH return-item risk. This was partly a
transitory problem that was naturally corrected as banks learned how to more effectively control
and manage the nontraditional sources of payment information and authorization involved in the

1

Direct deposits and recurring bill payments are both examples of the Prearrange Payment and Deposit Entry type
of ACH transaction. Each industry-recognized type of ACH transaction is assigned an official Standard Entry Class
(SEC) code. For example, the code for Prearranged Payment and Deposit Entry transactions is PPD. See Table 1
for a summary, including codes and definitions, of the ACH transaction types we analyze.

4

new types of payments. 2 However, some of the new risks were more fundamental, because the
new types of ACH consumer debits were intended to open the system to one-time and thus less
established, more anonymous debit-initiating customers, such as telephonic or online retailers.
The tremendous and predominantly successful broadening of the ACH system into these new
forms of business is testimony to the demand for and benefits of the new ACH consumer-debit
transactions introduced over the past decade. At the same time, the openness and flexibility of
the new transaction types has raised concerns about a growing risk to banks from irresponsible or
fraudulent consumer debits, such as those submitted by abusive retailers or outright scam artists
using call centers or web sites to obtain consumers’ account information and/or less-than-fully
informed consent.

To limit and manage these and other risks associated with ACH consumer debits, banks and
payments providers have responded with enhanced controls. Over time, these have reduced the
rate at which the new ACH transactions are returned for administrative errors or lack of proper
authorization, an indicator of potential fraud (Braun, McAndrews, Roberds, and Sullivan 2008).
Nonetheless, among the types of transactions typically used by American businesses, ACH debit
items remain second (after checks) in reports of attempted or actual payments fraud (Association
for Finance Professionals 2008).

Monitoring ACH return volumes has emerged as one of the key ACH risk controls, but the lack
of detailed benchmarks limits its effectiveness. After first reviewing the rationale for monitoring
returns, we present a three-part argument that a method to analyze ACH return data in more
detail can provide better benchmarks and thereby improve the effectiveness of return-item
monitoring as an ACH risk control. We begin our argument by explaining that current
benchmarks are ineffective. They consist mainly of high-level average return rates. What is

2

Return rates were initially quite high when the new nonrecurring telephone- or Internet-initiated ACH transaction
types emerged between 2000 and 2002 (Furst and Nolle 2005; Gerdes and Walton 2005; Braun, McAndrews,
Roberds, and Sullivan 2008; Thomas 2007; Holcomb 2003). With support from industry associations and ACH
service providers, banks responded by enhancing the controls applied to the new transaction types, cutting return
rates on telephone and Internet-initiated ACH transactions by factors of 4 and 8, respectively, between 2002 and
2004 (Furst and Nolle 2005). Nonetheless, average return rates on telephonic and some other new transaction types
have remained above the overall ACH consumer debit return rate.

5

needed are expanded benchmarks that give information about the entire distribution of ACH
return rates and reasons. These distributions should be computed for all banks and transactions
as well as for selected subcategories of transaction types or bank peer groups. Next, we explain
and illustrate a method for computing these expanded benchmarks. The illustrations use data on
FedACH consumer-debit items from mid-2006. Using these data, we match ACH returns to
their forwards and compute detailed information on the distribution of banks by return rates as
well as the distributions of banks and returns by return reasons. We compute these distributions
both overall (for all ACH consumer debits) and conditional on the type of debit. 3 Finally, we
examine these distributions and confirm that they provide more informative benchmarks, due to
the wide return-rate and return-reason disparities they reveal across ODFIs and transaction types.
Based on the magnitude of these disparities, we conclude that “one size fits all” benchmarks,
such as the commonly used 2.5 percent overall threshold or the 1.0 percent threshold for
unauthorized returns, do not fully support efficient monitoring of ACH returns. More detailed
return-rate distributions like those we compute would significantly improve the ability of banks
and bank supervisors to conduct their respective roles in the management of ACH return-item
risk.

Return Rate Monitoring Is Recognized as a Critical ACH Risk Control
Ex-post monitoring of return rates is an important part of the ACH risk control tool kit. 4 It does
not eliminate the need for controls such as careful due diligence before agreeing to process debits
for a customer, but it has its own important role. Up-front due diligence is never perfect, and
customers with no obvious initial risk factors may turn out to be problematic after a relationship
has been initiated. Other controls, such as automated edits of transactions and diligent staff
training, can mitigate risks that are already well-known, but they lag behind emerging types of
fraud and error. Monitoring of customer returns by financial institutions is thus an essential
backstop for spotting fraud when prevention fails and for identifying new types of fraud and

3

Although we do not present distributions by type of originating customer or third-party processor, the methods we
illustrate could be expanded to also provide this information.
4
Other return-risk controls include thoroughly applying know-your-customer (and your customers’ customers)
principles, requiring customers to adhere to ACH industry rules, improving staff training, automating blocks and
edits of certain transactions, and requiring certain customers to set aside funds to cover returns.

6

nonfraud risks as they emerge. Without monitoring, a specific ACH customer might originate a
large volume of unauthorized, possibly fraudulent consumer debits over an extended period of
time. This could easily give rise to a large dollar amount of subsequent returns, which could
result in significant losses for the financial institution if it is unable to successfully charge them
back to the customer. More broadly, lack of monitoring could allow multiple customers to
perpetrate a new type of fraud on a large scale for an extended period, multiplying the financial
institution’s exposure to ACH return-item losses. Similarly, monitoring of financial institution
return rates by industry organizations and regulators can identify institutions whose management
of ACH return risks is deficient, as evidenced by an inexplicably high level of returns that
indicate that the institution is unacceptably vulnerable to potential ACH return losses.

Because of its important role, the monitoring of ACH consumer debit returns is already a wellestablished risk-control practice. Individual banks have willingly incorporated it into their
controls, 5 and banking associations have stressed its importance to their members. 6 The
National Automated Clearing House Association (NACHA), an industry association and private
rule-making organization, has endorsed monitoring and requires steps to be taken when 1 percent
or more of a firm’s originations are returned as “unauthorized.” 7 The Electronic Payments
Network (EPN), a leading private-sector ACH operator, has identified monitoring of ACH
returns as one of its six recommended ACH risk management tools 8 and provided a service that
notifies an ODFI when its unauthorized ACH payments exceed certain thresholds. (Furst and
Nolle 2005; FedACH offers a similar service.) NACHA itself receives data periodically from
the two major ACH operators (EPN and the Federal Reserve Banks) and has used the data to
identify banks with unusually high ACH return rates. NACHA rules also require ODFIs to
5

As in Wells Fargo’s 2002 “war” on ACH fraud; see
http://www.nacha.org/achnetwork/ach_quality/wellsfargo_db.doc.
6
The view that “Monitoring return activity is critical” and “can supply an early notification that there are problems
with the business practices of an originator that may have slipped through the initial screening process” was put
forth to bankers in Thomas (2007).
7
For example, NACHA gives monitoring an explicit role in “ongoing requirements” and “ACH operator tools,” two
of the five components of its recommended ACH risk management strategy. See NACHA (2007). For NACHA’s
rule on unauthorized returns exceeding 1 percent, see the March 21, 2008 entry at
http://www.nacha.org/ach_rules/Rule_Making_Process/Recent_Ammendments_to_Rules/recent_ammendments_to_
rules.htm.
8
Memo from George F. Thomas, President of EPN, to EPN participants, entitled “A Critical Issue: Managing the
Risk of ACH Debit Entries.” The memo’s language closely paralleled an earlier letter to banks from the Federal
Reserve Bank of Dallas; see Holcomb (2003).

7

provide NACHA with detailed explanations when returns on a merchant customer’s TEL
transactions exceed 2.5 percent (Furst and Nolle 2005).

Perhaps most broadly, in 2006 the Office of the Comptroller of the Currency (OCC) published
guidance that national banks should report ACH return-rate information to their boards of
directors. 9 This guidance, which has since been extended to all federally insured banking
institutions by the Federal Financial Institutions Examinations Council, includes the following
specific recommendations (underlining added, one footnote omitted):

To oversee management’s execution of the ACH program effectively, the board of
directors, or a committee thereof, should receive periodic reports that allow the board to
determine whether ACH activities remain within board-established risk parameters and
are achieving expected financial results. Such reports generally include:
•

Metrics and trend analysis on ACH volume, returns, operational losses, and
transaction types, with explanations for variances from prior reports; …

•

A summary of return rates by originator, and, as applicable, third-party senders;

•

Unauthorized returns that exceed board-established thresholds; …

•

Risk management reports, including a comparison of actual performance to
approved risk parameters.

The guidance goes on to recommend that “Banks that engage in high-risk ACH activities should
… monitor the level of unauthorized returns … In addition, transactions with higher-risk
elements, such as TEL and WEB, should be monitored to ensure that they are within the
institution’s risk tolerance. A high level of unauthorized returns is often indicative of fraudulent
activity.” The guidance then cites NACHA’s operating guidelines, noting that “a return rate of
2.5 percent is well above the acceptable rate for normal business purposes.” 10

9

See “Automated Clearing House Activities: Risk Management Guidance,” OCC Bulletin 2006-39.
A 2005 ACH risk management white paper notes “Other than for unauthorized TEL transactions (2.5%
unauthorized data reporting requirement), the current NACHA Operating Rules and Guidelines are silent on
exception (returns) level thresholds and related monitoring/reporting requirements.” See “A New Strategic ACH
10

8

Crude Benchmarks Currently Limit Effective Monitoring

Although the monitoring of ACH return rates is widely endorsed, the data currently available on
actual ACH return rates provide very limited information. Institutions can look at their own data
in whatever detail they choose, but they have to rely on a few broad measures of average return
rates to assess whether their own experience is normal or unusual. As noted above, the OCC
cites NACHA’s 2.5 percent threshold as a benchmark for high-risk transactions generally,
specifically including WEB as well as TEL, even though the actual return rates for WEB are
much lower than for TEL (Furst and Nolle 2005). NACHA’s Risk Management News
periodically provides more information on average return rates by transaction type and return
reason. For example, the December 2006 issue of NACHA’s Risk Management News provided
average return rates by three reason codes (Insufficient Funds, or NSF; Unauthorized; and
Administrative) for all ACH items and nine additional subcategories. 11 To the best of our
knowledge, these data are among the most detailed available.

Averages provided by NACHA are helpful, but they tell very little about the variation of return
rates across institutions. As a result, averages allow fully effective monitoring only under some
restrictive conditions. First, since an individual institution will only know its own return rates
and NACHA’s industry average return rates, this information must be enough for it to know how
to respond. In particular, an institution that is 10 percent above average should respond more or
less the same as an institution that is 25 percent above average, since neither will know how
relatively far above average its own return rate is. Second, institutions should be very similar in
the type of ACH businesses they do, at least within the subcategories NACHA reports on. Under
these two conditions, all institutions need to know are industrywide averages by the
subcategories NACHA provides. They would have little use for information about their relative
position or for data on a more narrowly defined peer group. In short, unless banks are very
similar to each other in the types of ACH services they provide and the extent or significance of

Rules Framework for Risk Mitigation in the 21st Century,” a May 2005 White Paper prepared for NACHA by Two
Sparrow Consulting (p. 15).
11
All credits, all debits, Corporate Cash Disbursement credits, PPD debits, ARC, POP, RCK, TEL, and WEB. (For
descriptions of the last six subcategories listed here, see Table 1.)

9

their deviation from average return rates, average return rates by major categories provide only
very limited information to bank management about the relative level of ACH return risk the
bank is facing. 12

In the next section, we compute and analyze more detailed information about return rates than is
currently available (to our knowledge), for two purposes. First, we want to show that it is
practical to provide such information, which can be done by jointly analyzing transaction-level
data on forwards and returns and using the results to tabulate how ODFIs are distributed by ACH
return rates and return reasons. We tabulate results for all ACH return items as well as for returns
of only specific types of forwards, which results in conditional (on type of forward) return
distributions. Second, we argue that these overall and conditional distributions are valuable for
managing ACH risk. They show that banks differ greatly in both the types of ACH services they
provide and the extent to which their return rates exceed industry averages, even within fairly
precise subcategories. As just discussed, this implies that risks vary across institutions and that
average return rates are not sufficient for optimal ACH return-risk monitoring. Instead, what is
needed for more effective monitoring by and of ODFIs is information of the type we provide,
showing the entire distribution of ACH return rates by transaction/return-reason categories and
sometimes for specific peer groups as well. Jointly analyzing extensive microdata on returns and
forwards allows us to compute more complete ACH return rate benchmarks that can be
customized to meet a variety of ACH risk management needs.

More Detailed Benchmarks Can Be Computed

The remainder of this paper summarizes our analysis of an extensive sample of ACH debit
transactions from mid-2006. We begin this section with a little background on the ACH
consumer debit business, including the types of transactions and returns we will analyze. Then
we describe our data, which include both forwards and returns, and explain why and how we
matched return items to forward items. Using the matched returns and forwards, we tabulate a

12

In principle, industry associations and regulators might aggregate individual institution data so as to develop more
detailed assessments of how a given institution’s return rates compare to industry and peer group performance, but
in practice this would be difficult.

10

set of return-rate distributions for several categories and subcategories of forwards and returns, to
illustrate the detailed information that could be provided for more effective ACH return-rate
monitoring. In the subsequent “Results…” section, we argue that these distributions confirm that
monitoring based on average return rates alone is suboptimal, in part because return rate
distributions are highly skewed and return rates and return reasons differ significantly by
financial institution as well as by transaction type. We conclude with some thoughts on possible
further analysis.

Background. The ACH supports several types of consumer-debit transactions, as shown in Table
1. In a typical case, the ODFI creates a forward item, on behalf of one of its customers, for the
purpose of collecting funds from a consumer who banks elsewhere. The ACH system routes this
debit item to the RDFI thought to hold the account of the customer being debited. When the
transaction is successfully completed, payment is debited from the consumer’s account at the
RDFI and credited to the appropriate ODFI customer account.

However, somewhat less that 2 percent of time the process fails and the RDFI creates an ACH
return transaction, which notifies the ODFI that the debit has not been collected or needs to be
reversed. Each returned transaction must be classified using one of a large number of standard
NACHA return-reason codes. To condense the return-reason distribution we present below, we
have combined some of the detailed reason codes into broader categories, as shown in Table 2.
Typically, an Unauthorized return occurs because the consumer being debited reports that he or
she did not agree to the transaction; a low account balance causes an NSF return; a coding error
causes an Administrative return; and something about a Suspicious return suggests it might be
fraudulent.

Data. The data analyzed in this paper were initially drawn from two very large samples—all
consumer debits sent for collection through the Federal Reserve Banks’ FedACH processing
application from April 3, 2006, through June 30, 2006 (forwards), and all consumer debits
returned through FedACH from April 3, 2006, through September 29, 2006 (returns). This
sample provided 1.2 billion ACH transactions to analyze, which represent approximately threefourths of the total interbank ACH network volume of consumer debits during those periods (and
11

about five-eighths of all ACH consumer debits, including on-us and directly exchanged items not
processed through the interbank network). 13 With the exceptions noted below, we believe that
this is a representative sample.

Virtually all interbank ACH transactions in the United States flow through FedACH or EPN, the
two organizations that currently serve as ACH operators under the rules of NACHA. The
Federal Reserve Banks, using the FedACH application, process ACH transactions originated by,
delivered to, or returned to/by FedACH customers. When transactions are originated by
customers of one operator but destined for customers of the other operator, the two operators
exchange those transactions (called interoperator transactions). As a result, transactions
originated by EPN customers for delivery to FedACH customers flow through FedACH as well
as EPN, and vice versa. Consumer debits originated in EPN but bound for an RDFI in FedACH
are included in our data on forwards, as are forwards originated in FedACH but bound for an
EPN RDFI. We also capture returns crossing between FedACH and EPN. Because our sample
included all consumer ACH debit transactions in the FedACH daily transaction log during the
selection period, the only transactions omitted are those (1) where the ODFI and RDFI are both
EPN customers or (2) that do not enter the interbank system. 14

Our sample contains two parts: data from (1) 64 consecutive business-days of consumer debit
transactions forwarded in FedACH and (2) 127 consecutive business-days of consumer debit
transactions returned in FedACH. 15 Transactions can be returned as soon as the day they are

13

We thank Amanda Dorphy of the Federal Reserve Bank of Minneapolis for these estimates.
In 2006, an estimated 16.9 percent of ACH debit transactions were on-us, that is, between customers of a single
bank and processed by that bank independently of EPN or FedACH. This level was down from an estimated 20.6
percent on-us payments in 2003, but on-us transactions may have increased since 2006 due to bank consolidation. In
addition, in 2006, a very small number of interbank transactions—estimated at 0.3 percent—may have been
exchanged directly between banks (that is, not through any ACH operator). For the 2003 and 2006 data, see,
respectively, Dove Consulting 2004 (p. 17) and Dove Consulting 2008 (pp. 20–21).
15
ACH debit transactions have codes that indicate whether they are intended to draw funds from consumer
accounts, or whether they are intended as business-to-business transactions. Our sample included all debit
transactions that were intended to draw funds from consumer accounts, and excluded business-to-business
transactions. We did not get any information from FedACH that would permit identification of individual
consumers, nor did we have access to such confidential information.
14

12

presented or as much as 90 days later. 16 The returned-transaction sample was designed to cover
the period during which transactions from the forward sample were most likely to be returned.

Table 3 presents summary statistics on the volume of forwards and returns in our data. During
the roughly three months of the forward sample period, transactions amounting to almost $580
billion were debited to consumers’ accounts. Daily transaction values varied from $5 billion to
almost $20 billion. The daily number of transactions ranged from 11 million to 36 million,
averaging 19 million per day. The average value of a forward transaction was $478.

Although we collected returns for twice as long, the low fraction of items returned made the
volume of returned debit transactions much smaller, $18 billion. On a daily basis, return
transaction values varied from $100 million to just over $1 billion. The daily number of returned
transactions ranged from 200,000 to 700,000, averaging 400,000 per day with an average value
of $353.

Matching. The key to computing detailed, customized return-rate distributions is to jointly
analyze detailed data on forwards and returns. We do that by matching as many return items as
possible to their corresponding forward item. 17 This is challenging, because ACH file formats
do not ensure a unique identifier to link forwards and returns. NACHA rules specify the data
items and formats for ACH transactions, with some fields mandatory and others optional.
Neither a return nor its forward contains the full record of a returned transaction. We collected

16

According to the Federal Reserve Board’s Regulation E (Section 205.11), consumers can notify their depository
institution of the need to return a forward within 60 days after they receive their periodic (monthly) statement. This
means that forwards can be returned up to 90 days from the original processing date.
17
An alternative approach to computing the many ratios we analyze would be to use the same detailed transaction
data to compute statistics on forwards and returns separately, without matching individual returns to their forward
item. (For example, the numerator for a specific conditional return rate could be computed from the appropriate
group of return items, and the denominator could be computed from the appropriate group of forward items, without
matching items.) The no-matching approach has the advantage of including all forward and return items, whereas in
our approach we drop about 10 percent of the returns from our analysis because we are unable to match them to a
forward. However, there is also a positive side to dropping unmatched returns, since one reason some returns fail to
match with a forward is inaccurate data on the return, which our procedure weeds out. In addition, matched data is
essential for some purposes, such as analysis of how return rates are affected by the type of sending point (since
sending-point data only appears on forward items, not returns) or micro-econometric analysis of factors affecting
return rates. Since we don’t undertake these tasks in this paper, our use of matched data is primarily to expedite
computation of distributions and ratios and to weed out inaccurately coded and duplicate return items.

13

nine fields that are common to forwards and returns, in order to match them. We collected
additional fields (six from forwards and nine from returns) that provide information useful for
our analysis, such as the date of and reason for a return. We refer to the information from the set
of matched forwards and returns as the matched data set.

Some of our returns cannot be matched to forwards in our data due to discrepancies in data and
timing. According to FedACH staff, data discrepancies often arise from inaccurate coding of
returns, due to either manual entry errors or poorly designed software that fails to accurately
transfer information from the forward to the return. 18
With regard to timing, returns can be made for up to 90 days after a forward is processed. 19
Returns in our sample that occurred from April through June 2006 could therefore correspond to
forwards processed before April 3, 2006, the first date in our sample of forwards. Also, any of
the returns in our sample from July to September 2006 could correspond to forwards processed
after we stopped collecting forward transactions on June 30, 2006.

Forward items contain an effective entry date field intended to specify the date on which the
ODFI requests the forward item to settle. 20 This date should be, and usually is, copied from the
forward item to the return item. 21 We use it to delete return items whose entry dates do not
match any forward item in our data set. Eliminating returns whose effective entry date has no
match reduces the number of returns from an initial total of 51.9 million to a more relevant
subsample of 26.1 million. 22

18

We especially thank Joseph Fahnhorst of the Federal Reserve Bank of Minneapolis for his help in understanding
why returns and forwards may not match up.
19
As a practical matter, a small percentage of returns is processed beyond 90 days as well.
20
The information in the effective-date field on the forward item may depend on a manually entered date, and this is
thought to be one reason why effective entry dates are sometimes erroneous. We see this in our data. Some of our
forward items had effective entry dates before April 3, 2006, including as far back as 2001. We note, also, the
actual settlement sometimes occurs after the date requested in the effective entry date field.
21
However, according to Joseph Fahnhorst of the ACH staff of the Federal Reserve Bank of Minneapolis, manual or
software errors in transferring the effective entry date to the return are a common type of return item data error and
probably account for a large share of the returns that we were unable to match to forwards.
22
We know that many of the returns in our full dataset will not have a matching forward item in our data. We
collected returns for three month after we stopped collecting forwards, to capture items returned up to 90 days after
their entry date. Since most returns occur within the first week after the entry date, most of the returns during this
period did not match any of our forwards.

14

We then further restrict our sample to entry dates from April 4 to June 30, 2006, a period
beginning one day after we began collecting forwards. 23 Over the April 4 to June 30 period of
entry dates, where we have the best chance of uniquely matching a return to its forward, we have
a sample of 24.4 million returns.

From these 24.4 million returns, we use eight more data fields to seek unique matches. After
eliminating duplicates (returns with identical information in our nine data fields), we work with
24.2 million returns. Of these, 21.6 million match uniquely with forwards, for an overall match
rate of 89.4 percent. The match rate varies slightly from one entry date to the next, from a low of
86.6 percent on April 11 to a high of 95.7 percent on April 15. The unweighted average of the
daily match rates is 90.4 percent, and the standard deviation is 0.025. We consider these match
rates adequate for computing informative distributions of ACH return rates.

Results Confirm the Relevance of More Detailed Benchmarks for ACH Risk Management

Overview. The matched data computed with the method described above allow us to compute a
wide range of return-rate distributions. Here we review some of those distributions and argue
that the wide range of ODFI experience they show, their skewness, and the differences between
them imply that they provide much more useful ACH risk management benchmarks than the
simpler measures currently in use, such as average return rates. Our specific conclusions
regarding our 2006 data include:

1.

Overall and for most transaction types, return-rate distributions showed a wide range
of ODFI return rates and were significantly skewed. In particular, high return rates
among a minority of ODFIs caused average (mean) return rates to lie well above
typical (median) return rates. [See the remainder of this Overview subsection for
discussion of this point, with additional details in the other subsections below.]

23

This one-day initial lag reflects the fact that, due to ACH processing schedules and rules, many ACH consumerdebit items are processed at least one day in advance of their desired settlement date (the entry date). For this reason,
we would not expect our data to include the full population of FedACH forwards corresponding to returns with a 0403-2006 entry date.

15

2.

Overall and for most major transaction types, the majority of returns was originated
by a minority of ODFIs with well above-average return rates. [See the remainder of
this Overview subsection for discussion of this point, with an exception noted in the
ARC Results subsection and additional details in the other subsections below.]

3.

Overall and for transaction types with a large number of participating ODFIs, return
rates were not especially high at medium-volume, and to some extent small-volume,
originators. Similarly, overall and for most transaction types with widespread ODFI
participation, return rates differed little by the deposit size of the ODFI or, if
anything, were often somewhat lower at smaller institutions. That is, our results do
not support concerns that have been expressed about ACH return risk management
being relatively lax at typical small- or medium-sized ODFIs. [See the PPD Results
subsection for the primary discussion of this point, the ARC Results subsection for an
exception, and other subsections and Appendix Two for further details.]

4.

TEL transactions seemed to have generally greater return risk than most other
transaction types, across the full distribution of TEL originators. [See the TEL and
WEB Results subsection.]

5.

By contrast, WEB transactions had low to normal return risks for most originators but
appeared somewhat riskier overall due to high return rates at a minority of
originators, many of whom originated in large volume. [See the TEL and WEB
Results subsection.]

6.

When a consumer debit item was returned, insufficient funds were the reason 60 to 80
percent of the time, for most transaction types and usually also for ODFIs of all levels
of return rates. The next most common reasons involved administrative problems
(about 10 to 15 percent) or potentially suspicious activity (about 10 to 12 percent).
[See the Results for Return Reasons subsection.]

7.

There were some exceptions to the general pattern of return reasons, notably that TEL
and WEB originators with low return rates had an unusually small percentage of
items returned for insufficient funds. [See the Results for Return Reasons
subsection.]

16

The first conclusion above is evident in Table 4, whose first row shows key points in the
distribution of total consumer debit return rates across the population of over 8,500 ODFIs in our
matched data set. 24 Note that the average, or mean, rate of forwards that were returned was 1.8
percent in our data, very much in line with other estimates of overall ACH return rates.
However, also note that the mean rate was well above the median rate of 0.7 percent and
essentially equals the 75th percentile of the distribution. Thus, the distribution of overall return
rates across ODFIs was significantly skewed, with about 75 percent of ODFIs experiencing
below-average return rates. The minority of ODFIs with above-average return rates lifted the
overall mean to more than twice the median rate of return. In the tails of the distribution, return
rates became quite high. More than 5 percent of ODFIs had overall return rates in excess of 6
percent, and at least some ODFIs (generally those with a very small number of forwards) had
100 percent of their forwards returned.

Because many of the extremely high and low return rates reflected in Row 1 of Table 4 were
associated with ODFIs that originated only a small number of forwards, we also show, in Row 2,
the overall distribution limited to ODFIs that originated, for at least one transaction type, 100 or
more forward items. 25 We refer to this more limited but perhaps more meaningful sample as our
baseline data. Using these data, the distribution was somewhat less skewed but nonetheless
reconfirmed the first conclusion above. The mean rate of return, 1.6 percent, remained well
above the median, 0.9 percent, and extreme results were not eliminated. Five percent of this
population of ODFIs had return rates of 4.9 percent or higher, and the highest single ODFI had
over 80 percent of its 100+ forwards returned. This diversity of performance suggests we should
look in more detail at what might have given rise to such different outcomes.

24

ACH transactions are initiated through a depository institution’s (DI) account as referenced by an ABA (routingtransit) number. Individual DIs may have more than one such account. In our analysis, we aggregate all
transactions across all accounts owned by the topmost holder, either a chartered DI or a bank holding company.
These institutions encompass multiple charter types including commercial banks, credit unions and thrifts. All
counts of ODFIs in our results, including the 8,500 total referenced here, are of topmost holders only.
25
To a tenth of a percent, Row 2 would be exactly the same if we instead used the set of all ODFIs with 100 or more
total forwards of any kind. The two sets of ODFIs are nearly identical; as nearly all ODFIs with 100+ total forwards
also originated 100+ PPD forwards. In fact, either set omits fewer than 0.01 percent of all the forwards in our
matched dataset; the two sets differ only by 0.0001 percent of the total forwards. This is convenient, because Table 5
is based on groups of ODFIs with 100+ forwards of a specific type. Using the same set of ODFIs to construct Table
4 facilitates comparisons between these two tables without changing the results, up to a tenth of a percent.

17

Table 5 provides some of the relevant detail. Panel A repeats (for reference) the second row of
Table 4 and provides further details of the overall distribution of ACH consumer-debit returns.
Row 3 in Panel A substantiates the second conclusion above, by showing that the lowest half of
the distribution—ODFIs with return rates at or below the median return rate of 0.9 percent—
accounted for only 9.9 percent of all the ACH returns in our baseline data. By contrast, the
upper 25 percent of the distribution—ODFIs with return rates above 1.8 percent—accounted for
just over half of all returns (50.8 percent, or 100-49.2 percent). The highest 5 percent of the
ODFIs in the overall return-rate distribution accounted for over one-fourth (27.8 percent) of all
the returns items in our baseline data.

By substantiating our first two conclusions, the overall return rate distribution illustrated in Panel
A of Table 5 already provides improved benchmarks for ACH return-risk managers. With
percentiles tabulated in fine detail (a simple matter but not undertaken here to save space), this
distribution would show how far a given bank’s return rate lies above or below industry norms.
For banks with above-average return rates, both management and banking supervisors could
quickly see whether a bank’s return rate is merely slightly or quite distinctly higher than normal.
This capability could guide decisions about how quickly and forcefully to examine and possibly
address the underlying causes. These distinctions are much more difficult to make when average
return rates are the only common benchmark of ACH return risk.

To further illustrate the advantages of tabulating detailed return-rate distributions and
substantiate our other five conclusions, we rely mainly on Panels B through E of Table 5 and, to
some extent, on Appendix Three. They provide detailed information (in the same format as
Panel A) about ten conditional return rate distributions. That is, each of these distributions is
conditioned on (takes as its universe) a specific type of forward. To eliminate outliers caused by
ODFIs with minimal activity, in each panel we use include only ODFIs that originated at least
100 of that panel’s forwards in our matched data. For example, in Panel B (rows 9–16), we
examine return rates for Prearranged Payment and Deposit (PPD) forwards by ODFIs with at
least 100 PPD forward transactions in our matched data. For each type of consumer debit
transaction, we show not only the conditional distribution of return rates but also, in the last five

18

rows of each block, information about the reason codes associated with these returns. The
interpretation of the panels is further clarified in the discussion below.

PPD Results. PPD transactions are the most common type of ACH consumer debit transaction,
both generally and in our data. As shown by the count of ODFIs in each panel of Table 5 or in
Appendix Three, the 6,050 ODFIs with 100 or more PPD originations are also by far the
majority of the ODFIs whose return rates we analyze. For that reason, the overall ACH returnrate distribution was similar to the PPD return-rate distribution, as seen by the fact that rows 1
and 10 of Table 5 are nearly identical. Rows 10 and 11 show that our first and second
conclusions hold for PPDs as well. The mean return rate was well above the median, and ODFIs
in the lowest 75 percent of the return-rate distribution collectively originated less than 22 percent
of all PPD items returned. The remaining 25 percent of the PPD ODFIs accounted for over 78
percent of returned PPD items, and only 5 percent of the PPD ODFIs originated over 35 percent
of PPD items returned.

Row 11 suggests that PPD return rates were not especially low among high-volume originators,
in keeping with our third conclusion and contrary to concerns that have been expressed about lax
risk management at small- and medium-size originators. A closer look at return rates by volume
bears this out. We compared 4,073 medium-volume ODFIs (defined as originating between 100
and 2,000 PPD items in our matched data set) with 1,977 large-volume ODFIs (originating more
than 2,000 such items). The typical (median) ODFI in the medium-volume group had only a
moderately higher return rate than the median ODFI in the large-volume group, 1.0 compared to
0.9. The gap widened somewhat at the 75th percentile (1.9 versus 1.5) but reversed at the 95th
percentile (4.7 versus 4.9). A few of the 2,463 small PPD originators (ODFIs with fewer than
100 PPD forwards in our matched data set) had very high return rates, up to 100 percent, but this
is not surprising, given their small volumes. Nonetheless, the median small PPD originator had
no items returned, and its average return rate of 2.3 percent was not exceedingly higher than the
average return rates of 1.5 percent and 1.7 percent, respectively, among medium-volume and
large-volume PPD originators. Since PPD originators are by far the most common type of
originator we analyze, the same rough equality between smaller-volume and larger-volume

19

originators holds for the overall distribution of return rates for all ACH consumer debits, in Row
1.

Appendix Two shows that the same conclusion holds when banks are sorted by deposit size
rather than PPD volume. In fact, to the extent that return rates varied by bank size, small- and
medium-size banks often had somewhat lower rates, especially for transaction types with
widespread bank participation. For overall return rates, for example, the median rate for small
banks (under $500 million in deposits) was 0.9 percent, compared to 1.2 percent for large banks.
This is not just a compositional effect, as could arise if small banks are relatively less likely to
originate transaction types with higher return rates. For example, the median small-bank return
rates for TEL and WEB transactions were 4.0 percent and 0.3 percent, respectively, while the
corresponding large-bank figures were higher, 4.7 percent and 1.4 percent, respectively.

ARC Results. Accounts Receivable Entry (ARC) transactions arise when data from a paper
check received in the mail is used to create an ACH debit item. Specialized “lockbox”
processors typically perform this service, for example on checks that consumers have mailed to
pay their credit cards or utility bills. ARC transactions were the second most common type of
forwards in our benchmark data, after PPDs. Table 5 shows that, quite unlike PPD, the ARC
business was highly concentrated, with only 148 ODFIs originating 100 or more ARC forwards
in our three-month sample of forwards. ARC return rates (Row 18) were generally low to
moderate, at least compared to overall ACH return rates (Row 1). ARC is an exception to our
second conclusion, that a minority of originators with high return rates generally accounted for
the majority of returns. Row 19 of the table shows that, by number, over 93 percent of ARC
returns came from ODFIs whose return rates were at or below the median return rate of 0.6
percent. 26 As this suggests, ARC was also an exception to our third conclusion, that return rates
for many transaction types did not vary significantly with ODFIs’ volume of transactions. For
ARC, larger volume was associated with lower return rates. Medium-size (100 to 2,000
forwards) ARC originators had mean and median return rates of 2.2 percent and 1.3 percent,

26

Appendix Three shows that POP transactions (point-of-purchase debits based on the customer’s written
authorization, as for a check conversion) are a similar exception.

20

respectively, whereas the mean and median return rates for large (more than 2,000 forwards)
ARC originators were less than half as high, 0.9 percent and 0.5 percent, respectively.

TEL and WEB Results. Table 5 shows some risk-relevant differences between the return rate
distributions of two newer ACH transaction types, TEL and WEB. These transactions serve a
similar function—permitting one-time debits to be created from the same information as on a
check but instead provided by the account owner via telephone (TEL) or over the Internet
(WEB). Both had above-average return rates—their mean return rates exceeded the overall
mean return rate for ACH consumer debits—and due to this tendency, and because they both
accommodate one-time, consumer-initiated transactions, they are often lumped together in
discussions of ACH return risks. However, our analysis of their full return-rate distributions
suggests differences in the nature of their risks and in how those risks might be reduced.

The elevated mean return rate for TEL was part of a broader pattern summarized in our fourth
conclusion above—the distribution of TEL returns (Row 26) was higher than the overall
distribution (Row 1), at least through the 95th percentile. The median TEL ODFI experienced a
4.0 percent return rate, more than four times higher than the median ODFI return rate for overall
ACH transactions, and return rates for TEL were similarly higher at the 25th, 75th, and 95th
percentiles of the distribution. 27 In other words, TEL transactions appeared to be inherently
riskier than most ACH consumer debit transactions.

By contrast, the lower three-fourths of the WEB distribution (in Row 34) displayed somewhat
lower return rates than the overall ACH return rate distribution. However, in the upper fourth of
the WEB distribution, return rates were higher than in the overall ACH return-rate distribution,
so that at the 95th percentile WEB originators experienced almost a 50 percent higher return rate
than at the 95th percentile of the distribution of all ACH consumer debits. This was the basis for
our fifth conclusion above—that WEB transactions were not inherently more risky, as TEL
transactions appeared to be, but rather experienced an above–average mean return rate because a

27

This is not true at the 100th percentile, due to an outlier in the distribution of PPD returns.

21

small minority of WEB originators failed to match the generally low return rates of the vast
majority of WEB originators.

Obviously, from a risk management perspective, both TEL and WEB ODFIs with high return
rates might benefit from emulating ODFIs with lower TEL and WEB return rates. However, the
net results might differ. For WEB, adoption of better practices by a minority of high-return-rate
ODFIs might make WEB a below-average return rate transaction type overall. Based on our
data, this would not be the case for TEL, as most of the TEL originators in the lower half of the
TEL return-rate distribution experienced TEL return rates above the overall mean ACH return
rate. These insights are easily seen in the distributions we have tabulated but cannot be derived
from simple summary statistics such as average return rates.

Results for Other ACH Consumer Debits. Appendix Three provides similar details about the
distribution of returns for six more transaction types. We invite the reader to confirm that, for
the most part, Appendix Three supports the conclusions above, although there are some
exceptions. We note, however, that only a small number of financial institutions originate some
of these types of transactions, so that their conditional return distributions may not be very
precise or indicative of what would prevail if participation expanded significantly.

Results for Return Reasons. Table 5 also shows the main reasons why ACH consumer debits
were returned and how the reasons varied across transaction types and ODFIs. For all ACH
consumer debits, Rows 4 to 8 substantiate part of our sixth conclusion above—that insufficient
funds were the main reason for returns overall and that the prevalence of the insufficient funds
reason was more or less the same among ODFIs with low, medium, or high return rates. 28
Insufficient funds account for about 70 percent of all returned items in our matched data, and this
percentage does not vary much with return rates. The next most common return reasons involve
either administrative problems (10 to 15 percent of returns across low- to high-return-rate

28

Although many items returned for insufficient funds were authorized by the account holder, this category can also
include unauthorized or fraudulent debits that exceeded the account balance.

22

ODFIs) or a set of reasons we have labeled “suspicious” (10 to 12 percent across low- to highreturn-rate ODFIs). 29

The pattern of return reasons for most of the individual consumer debit transaction types in Table
5 or Appendix Three was similar to the overall pattern. However, we also see the pattern
summarized in our conclusion seven—for TEL and WEB transactions initiated by ODFIs with
relatively low TEL or WEB return rates, respectively, insufficient funds explained less than half
of the items returned, and administrative errors explained over 40 percent. However, among
higher-return-rate TEL and WEB originators, a more typical pattern, dominated by insufficient
funds returns, prevailed. This further illustrates the potential utility of examining full return-rate
and reason-code distributions, for an analysis of why TEL and WEB originators with low return
rates were relatively less prone to insufficient-funds returns might shed light on how these
originators achieved their lower TEL and WEB return rates.

Concluding Remarks

The results highlighted above show how conditional return distributions can provide insights
beyond what can be inferred from simple summary statistics like the mean return rate. Most
importantly, this study shows that the typical summary statistics do not adequately summarize
the diversity of return experiences across transaction types and ODFIs. Accordingly, ACH
return-monitoring systems based on overall mean return rates or even mean return rates for
selected transaction types cannot be fully efficient. Too many details about the range and
skewness of the return-rate distributions, behaviors in the upper and lower extremes of the
distributions, and the relationships between return rates and forward volumes or return reasons
are ignored in systems that focus only on mean return rates. By utilizing more detailed overall
and transaction-specific distributions, such as those in Table 5, individual ODFIs and their

29

The fact that over 1 percent of consumer debit returns were unauthorized does not mean that the typical ODFI
exceeds NACHA’s 1 percent threshold for unauthorized returns. That threshold applies to all forwards, whereas the
reason-code percentages in Table 5 are only for returns. For example, for the median ODFI in Panel A, only about
900 of every 100,000 consumer debit forwards are returned. Of those, less than two percent, or about 17, are
returned as unauthorized. Thus, the rate of forwards returned as unauthorized for the median ODFI in Panel A is
about 17 per 100,000, or 0.017 percent, far less than NACHA’s threshold.

23

regulators and industry associations would have a much clearer picture of how an ODFI’s ACH
return-risk exposure compares to that of its industry peers and possibly also see clues into the
factors that allow some ODFIs to achieve low rates of return.

Our methods could be used to generate many other customized tables of return-rate and reasoncode distributions. One can, for example, tabulate charts and tables showing the distributions of
return rates and reason codes by ODFI or RDFI charter type, location (and the demographics of
the local area), and regulatory rating as well as by transaction size and timing (day of the week or
month). As one final example from our data, Table 6 shows both the distribution of the timing of
ACH returns (for all types of consumer debit forwards) and how the mix of reason codes shifted
toward Unauthorized as time passed. Over 98 percent of all returns were processed within 5
days, and over 80 percent of these returns were due to insufficient funds or administrative
problems. The remaining returns were mostly processed between 6 and 60 days after their
forward item was processed, and mostly because they were unauthorized. Very few returns took
more than 60 days, but the small fraction processed after 90 days included an unusually high
percentage returned for the miscellaneous reasons grouped in the “Other” category. If desired,
we could also compute how the percentage of returns processed within 5 days is distributed
across ODFIs, so that ODFI management or regulators could be alerted if an institution’s returns
tended to be much later than its peers or more tilted toward Unauthorized or other unusual
reasons. This could also be done by type of forward, for customized ODFI peer groups, and
more. The relevant criteria should be whatever statistics provide useful comparisons for ACH
return-item risk managers at ODFIs, RDFIs, and their regulators.

More broadly, we have shown, using FedACH data on consumer debits from mid-2006, that it is
possible to match most returns to their unique forward item. The resulting dataset of matched
forwards and returns can efficiently provide a rich array of benchmarks for ACH return-item
monitoring, including detailed, customized conditional distributions of return rates and reasons.
Such benchmarks would support more efficient and informative ACH return monitoring than is
possible with the relatively crude benchmarks in common use today.

24

Although our procedures and statistics took considerable time to develop and compute, we think
that they could be replicated quickly and efficiently on an ongoing basis if desired. This would
allow benchmarks such as those illustrated here to be updated frequently. In addition, regulators
or ACH operators would be able to identify, nearly in real time, ODFIs and RDFIs whose return
activity was well outside of appropriate peer-group norms. Finally, we have not attempted, so
far, to provide benchmarks for the return rates of the individual payments originators or thirdparty processors served by the ODFIs and RDFIs in our sample, but our methods could also be
applied to these entities to support monitoring of their activity. In general, our procedures could
be tailored and customized to provide the detailed ACH return-rate benchmarks needed for
improved monitoring of existing and emerging ACH return-item risks.

25

Table 1: The Types of ACH Consumer Debit Transactions Analyzed in this Paper
Consumer Debit
TransactionType

SEC
Code

SEC Code Description

Prearranged Payment
and Deposit

PPD

Pre-authorized debits to a consumer’s account, such as for
payment of one-time or recurring bills.

Accounts Receivable
Truncated Check

ARC

An ACH debit of a check received in the U.S. Mail and
converted to an electronic item. One prominent use is in
“lockbox” operations that process checks that consumers mail to
pay credit card and other bills. (The definition of U.S. Mail
includes mail delivered by the United States Postal Service as
well as mail delivered via courier service, including but limited
to Federal Express, United Parcel Service, or other local courier
service and does not include items personally delivered or
deposited in a merchant’s night drop.)

Telephone-Initiated
Entry

TEL

Single-entry debit transactions to a consumer’s account pursuant
to an oral authorization obtained from the consumer via
telephone. (This type of transaction may only be used when there
is no standing authorization for the origination of ACH entries to
the receiver’s account and may only be originated when there is
either (1) an existing relationship between the originator and the
receiver, or (2) no existing relationship between the originator
and the receiver, but the receiver has initiated the telephone call.)

Internet-Initiated Entry

WEB

A debit entry to a consumer account initiated by an originator
pursuant to an authorization that is obtained from the receiver via
the Internet.

Point-of-Purchase

POP

Non-recurring debit entries initiated by the originator based on a
written authorization and account information drawn from the
source document (a check) obtained from the consumer at the
point of purchase. Also known as ECC (Electronic Check
Conversion).

Point-of-Sale Entry

POS

Point-of-sale debits in a non-shared network. These transactions
are most often initiated by the consumer via a plastic access card.

Machine Transfer Entry

MTE

ACH debits authorized at ATMs.

Re-presented Check

RCK

An ACH debit used by originators to re-present a check that has
been processed through the check-collection system and returned
because of insufficient or uncollected funds.

Shared Network
Transaction

SHR

Point-of-sale debits in a shared network. These transactions are
most often initiated by the consumer via a plastic access card.

Destroyed Check Entry

XCK

A debit for the collection of certain checks, when those checks
have been destroyed.

Source: “ACH SEC Code Reference” web page, Alliance Payment Technologies, Inc. (APT). Accessed
at www.allianceach.com on January 21, 2009. The contents of the page are based on information
published in NACHA’s annual ACH Rules guides. (Note: As of March 2010, the APT web site is no
longer available.)

26

Table 2: Broad ACH Return Reason Categories
Broad Return Reason Label
(As designated by the authors)

NACHA Code

NACHA Return Reason Description

Unauthorized
R05

Unauthorized debit to consumer account using
corporate SEC Code

R07

Authorization revoked by consumer

R10

Customer advises not authorized

R29

Corporate customer advises not authorized

R03

No account/unable to locate account

R04

Invalid account number

R01

Insufficient funds

R09

Balance exists for current transaction but
value of transaction in process brings balance
below the debit entry

R02

Account closed (by customer or RDFI)

R16

Account frozen

R20

Non-transaction account

R51

Item is ineligible, notice not provided,
signature not genuine, item altered, amount of
entry not accurately obtained

Administrative

Insufficient Funds (NSF)

Suspicious

Other

All other codes

Source of NACHA codes and return reason descriptions: 2006 ACH Rules: A Complete Guide to Rules &
Regulations Governing the ACH Network, NACHA, 2006, p. OR 92–OR 98.

27

Table 3: The Volume of Transactions in Our Sample
Forwards
Number of days

Returns
64

127

1,210.4

51.9

Daily average volume

18.9

0.4

Highest volume day

35.6

0.7

Lowest volume day

11.0

0.2

578.1

18.3

9.0

0.1

Highest value day ($billions)

19.5

1.3

Lowest value day ($billions)

5.1

0.1

Average transaction value ($)

477.6

352.6

Maximum value ($millions)

100.0

100.0

0.01

0.01

Volume (in millions of transactions)
Total volume

Value
Total value ($billions)
Daily average value of transactions
($millions)

Minimum value ($)

Table 4: Overall Return Rates for FedACH Consumer Debits
(Based on 21.6 million returns matched to forwards with entry dates 4/4/06 to 6/30/06.)

All ODFIs
ODFIs with 100+ Forwards

Mean
1.8%
1.6%

Distribution Moments and Percentiles
25th
Median
75th
95th
100th
0.0.%
0.7%
1.8%
6.1% 100.0%
0.4%
0.9%
1.8%
4.9%
80.2%

28

Table 5: Distribution of Return Rates for ODFIs with 100+ Forwards,
for All ACH Consumer Debits and Selected Transaction Types and Return Reasons
(Based on 21.6 million returns matched to forwards with entry dates 4/4/06 to 6/30/06.)
Forward Item Categories
Distribution Moments and Percentiles
Row
Mean 25th Median 75th 95th 100th
No. Panel A: All Forwards
1.6
0.4
0.9
1.8
4.9
80.2
1 Overall return rate (%)
190,144
297
929 3,679 40,339 309,187,477
2 Total forwards
% of all returns by banks with overall return rates at or
24.9
0.6
9.9
49.2
72.2
100.0
3 below specified moments or percentiles of the distribution
Unauthorized
1.8
1.6
1.9
1.9
1.9
2.1
4
NSF
69.7 73.7
69.4
71.1
71.8
72.2
5 Return reason percentages, for banks with
Administrative
15.8 10.2
15.8
14.8
13.0
11.1
6 overall return rates at or below specified
Suspicious
10.4 12.6
10.5
10.2
11.3
12.7
7 moments or percentiles of the distribution
Other
2.3
1.9
2.5
2.0
2.0
1.9
8

By Panel B: PPD Returns
SEC PPD Conditional return rate (%, for 6,050 banks
10
with PPD forwards)
Code
9

11
12
13
14
15
16
17

% of PPD returns by banks with PPD return
rates at or below specified moments or
percentiles
Unauthorized
Return reason percentages,
NSF
for banks with PPD return
Administrative
rates at or below specified
Suspicious
moments or percentiles (5,462
Other
banks with PPD returns)

19
20
21
22
23
24
25

Panel D: TEL Returns

27
28
29
30
31
32

TEL Conditional return rate (%, for 339 banks with
TEL forwards)
% of TEL returns by banks with TEL return
rates at or below specified moments or
percentiles
Unauthorized
Return reason percentages,
NSF
for banks with TEL return
Administrative
rates at or below specified
Suspicious
moments or percentiles (335
Other
banks with TEL returns)

33

Panel E: WEB Returns

26

WEB Conditional return rate (%, for 452 banks with

34

35
36
37
38
39
40

0.4

0.9

1.8

4.8

80.2

19.5

1.0

5.6

21.4

64.9

100.0

2.6
67.1
12.9
14.4
3.0

2.1
74.7
7.0
13.6
2.6

2.3
68.4
12.7
13.8
2.7

2.6
67.3
12.8
14.3
3.0

2.4
72.3
10.2
12.8
2.4

2.3
73.2
8.3
14.1
2.1

1.4

0.3

0.6

1.5

4.4

18.0

97.8

73.8

93.3

97.8

99.9

100.0

0.9
66.9
23.2
5.2
3.8

0.9
65.2
24.8
5.5
3.5

0.9
66.0
24.0
5.3
3.9

0.9
66.9
23.2
5.2
3.8

0.9
66.5
23.4
5.2
4.0

0.9
66.4
23.4
5.2
4.0

5.5

2.3

4.0

6.3

17.0

41.4

58.8

0.2

3.2

72.8

86.3

100.0

1.7
71.6
19.5
5.9
1.3

2.0
47.8
44.8
3.9
1.5

2.2
61.9
27.8
6.4
1.8

1.7
71.3
19.6
6.0
1.4

1.8
70.8
19.6
6.3
1.5

2.0
70.3
18.0
7.8
1.8

2.0

0.1

0.6

1.7

7.1

73.6

59.4

0.0

15.4

25.5

76.1

100.0

2.2
66.6
17.4
12.6
1.3

0.0
38.7
48.4
8.1
4.8

1.4
74.3
12.4
10.7
1.2

1.6
69.4
17.6
10.2
1.2

2.5
67.5
16.6
12.1
1.4

3.2
67.0
15.5
12.9
1.5

Panel C: ARC Returns
ARC Conditional return rate (%, for 148 banks with
ARC forwards)
% of ARC returns by banks with ARC return
rates at or below specified moments or
percentiles
Unauthorized
Return reason percentages,
NSF
for banks with ARC return
Administrative
rates at or below specified
Suspicious
moments or percentiles (143
Other
banks with ARC returns)

18

1.6

WEB forwards)
% of WEB returns by banks with WEB return
rates at or below specified moments or
percentiles
Unauthorized
Return reason percentages,
NSF
for banks with WEB return
Administrative
rates at or below specified
Suspicious
moments or percentiles (362
Other
banks with WEB returns)

29

Table 6: ACH Consumer Debit Returns by Time and Reason
(Based on 21.6 million returns matched to forwards with entry dates 4/4/06 to 6/30/06.)
Days Before
Return

% of Total Returns
Within Time Frame

1 Day

19.04

2–5 Days

79.58

6–10 Days

0.41

11–30 Days

0.61

31–60 Days

0.33

61–90 Days

0.02

> 90 Days

0.00

Return Reason
Unauthorized
NSF
Administrative
Suspicious
Other
Unauthorized
NSF
Administrative
Suspicious
Other
Unauthorized
NSF
Administrative
Suspicious
Other
Unauthorized
NSF
Administrative
Suspicious
Other
Unauthorized
NSF
Administrative
Suspicious
Other
Unauthorized
NSF
Administrative
Suspicious
Other
Unauthorized
NSF
Administrative
Suspicious
Other

30

Share of Time Frame Returns
for Given Reason (%)
1.00
68.99
15.82
12.36
1.84
0.78
74.09
10.25
12.98
1.90
85.61
7.93
1.39
2.05
3.02
96.69
0.97
0.25
0.33
1.76
96.07
0.54
0.15
0.20
3.05
91.69
1.64
0.25
0.39
6.03
58.86
0.95
0.14
0.41
39.65

References

Association for Finance Professionals (2008), 2008 AFP Payments Fraud and Control Survey:
Report of Survey Results (March).

Braun, Michele, James McAndrews, William Roberds, and Richard Sullivan (2008),
“Understanding Risk Management in Emerging Retail Payments,” Federal Reserve Bank of New
York Economic Policy Review, (September): 136–159.

Dove Consulting (2004), 2004 Electronic Payments Study for Retail Payments Office at the
Federal Reserve Bank of Atlanta: Study Methods and Results Summary Report (December 14).
Available at http://frbservices.org/files/communications/pdf/research/2004EPStudy.pdf.

——(2008), The Electronic Payments Study: A Survey of Electronic Payments for the 2007
Federal Reserve Payments Study (March). Available at
http://frbservices.org/files/communications/pdf/research/2007_electronic_payments_study.pdf.

Furst, Karen and Daniel E. Nolle (2005), “What’s Your Risk with the Growing Use of ACH
Payments?” Office of the Comptroller of the Currency Quarterly Journal, v. 24, no. 4
(December): 21–43.

Gerdes, Geoffrey R. and Jack K. Walton (2005), “Trends in the Use of Payments Instruments in
the United States,” Federal Reserve Bulletin (Spring): 180–201.

Holcomb, Helen E. (2003), Managing the Risk of ACH Debit Entries, Federal Reserve Bank of
Dallas Notice 03-36 (July 21).

NACHA (2007), A Comprehensive Strategy for Risk Management in the ACH Network
(February 20).

31

Thomas, George (2007), “Not Your Father’s ACH,” Independent Community Bankers
Association Independent Banker (July): 95–97.

32

Appendix One: A Method for Matching ACH Forwards and Returns
Under NACHA rules, the following nine fields should be copied without modification from the
forward to the return:

Forward

Return

Amount

Amount

Standard Entry Class (SEC) code

Standard Entry Class (SEC) code

Company ID

Company ID

Company Name

Company Name

DFI Account

DFI Account

Effective Entry Date

Effective Entry Date

ODFI ABA

RDFI ABA

RDFI ABA

Original Recipient

Trace Number

Original Forward Trace Number

Using standard methods for joining tables in relational databases on multiple fields, we match
forwards and returns on these nine fields. Even the combination of these nine fields did not
always yield unique matches. In a relatively small number of cases, a return appears to match
multiple forwards, multiple returns appear to match a forward, or multiple returns appear to
match multiple forwards. Forwards and returns for which we could not ascertain a “proper”
match were excluded from the matched set.

33

Appendix Two: ACH Return Rates by Bank Deposit Size
(See Table 1 for definitions of the SEC codes shown below.)
Part 1: Return Rates for Small Banks (under $500 million in Deposits) with More Than 100
Forwards for at Least One SEC Code
Forward Item Categories

Distribution Moments and Percentiles
Mean 25th Median 75th 95th 100th

All Forwards
Overall Return Rate
Total Forwards
% of all returns by banks with overall return rates at or below
s pecified moments or percentiles of the distribution
By PPD Conditional return rate (%, for 4,883 banks with >
100 PPD forwards)
SEC
% of PPD returns by banks with PPD return rates at
Code
or below specified moments or percentiles
ARC Conditional return rate (%, for 51 banks with > 100
ARC forwards)
% of ARC returns by banks with ARC return rates
at or below specified moments or percentiles
TEL Conditional return rate (%, for 162 banks with > 100
TEL forwards)
% of TEL returns by banks with TEL return rates at
or below specified moments or percentiles
WEB Conditional return rate (%, for 247 banks with > 100
WEB forwards)
% of WEB returns by banks with WEB return rates
at or below specified moments or percentiles
POP Conditional return rate (%, for 8 banks with > 100
POP forwards)
% of POP returns by banks with POP return rates at
or below specified moments or percentiles
POS Conditional return rate (%, for 2 banks with > 100
POS forwards)
% of POS returns by banks with POS return rates at
or below specified moments or percentiles
MTE Conditional return rate (%, for 157 banks with > 100
MTE forwards)
% of MTE returns by banks with MTE return rates
at or below specified moments or percentiles
RCK Conditional return rate (%, for 28 banks with > 100
RCK forwards)
% of RCK returns by banks with RCK return rates
at or below specified moments or percentiles
SHR Conditional return rate (%, for 12 banks with > 100
SHR forwards)
% of SHR returns by banks with SHR return rates
at or below specified moments or percentiles
XCK Conditional return rate (%, for 1 bank with > 100
XCK forwards)
% of XCK returns by banks with XCK return rates
at or below specified moments or percentiles

34

1.5
9,238

0.3
251

0.9
646

1.8
4.8
80.2
2,069 11,549 5,333,866

3.1

0.5

1.4

3.5

16.5

100.0

1.5

0.4

0.9

1.8

4.7

80.2

4.1

0.4

1.9

5.1

15.6

100.0

1.9

0.4

0.9

2.0

7.8

18.0

82.9

33.7

55.5

83.2

99.4

100.0

5.2

2.3

4.0

6.9

13.0

34.3

10.7

0.4

0.6

58.8

78.6

100.0

1.8

0.0

0.3

1.1

6.6

73.6

3.2

0.0

0.0

0.8

29.0

100.0

2.6

1.5

2.1

3.6

5.6

5.6

95.0

0.5

92.9

95.2

100.0

100.0

1.2

0.9

1.2

1.5

1.5

1.5

38.9

38.9

38.9

100.0

100.0

100.0

0.0

0.0

0.0

0.0

0.0

0.1

4.2

0.0

0.0

0.0

44.1

100.0

49.2

41.8

49.4

60.9

72.3

91.9

13.7

0.2

13.7

77.3

99.7

100.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

100.0

100.0

17.9

17.9

17.9

17.9

17.9

17.9

100.0

100.0

100.0

100.0

100.0

100.0

Appendix Two (continued)
Part 2: Return Rates for Medium-Sized Banks ($500 million to $2 Billion in Deposits) with
More Than 100 Forwards for at Least One SEC Code*
Forward Item Categories

Distribution Moments and Percentiles
Mean 25th Median 75th

All Forwards

Overall Return Rate
1.7
Total Forwards
47,300
% of all returns by banks with overall return rates at or below
24.5
specified moments or percentiles of the distribution
by PPD Conditional return rate (%, for 801 banks with >
1.6
100 PPD forwards)
SEC
% of PPD returns by banks with PPD return rates
Code
31.7
at or below specified moments or percentiles
ARC Conditional return rate (%, for 30 banks with > 100
1.5
ARC forwards)
% of ARC returns by banks with ARC return rates
97.1
at or below specified moments or percentiles
TEL Conditional return rate (%, for 83 banks with > 100
4.7
TEL forwards)
% of TEL returns by banks with TEL return rates
10.4
at or below specified moments or percentiles
WEB Conditional return rate (%, for 90 banks with > 100
1.8
WEB forwards)
% of WEB returns by banks with WEB return rates
17.4
at or below specified moments or percentiles
POP Conditional return rate (%, for 7 banks with > 100
3.1
POP forwards)
% of POP returns by banks with POP return rates
19.2
at or below specified moments or percentiles
POS Conditional return rate (%, for 1 bank with > 100
1.0
POS forwards)
% of POS returns by banks with POS return rates
100.0
at or below specified moments or percentiles
MTE Conditional return rate (%, for 3 banks with > 100
0.0
MTE forwards)
% of MTE returns by banks with MTE return rates
31.0
at or below specified moments or percentiles
RCK Conditional return rate (%, for 19 banks with > 100
52.6
RCK forwards)
% of RCK returns by banks with RCK return rates
70.8
at or below specified moments or percentiles
SHR Conditional return rate (%, for 1 bank with > 100
0.0
SHR forwards)
% of SHR returns by banks with SHR return rates
100.0
at or below specified moments or percentiles

95th

1.0
5,621

1.8
5.0
41.3
14,736 75,514 7,280,753

1.9

15.9

24.7

45.7

100.0

0.6

1.0

1.7

4.6

38.5

3.2

25.6

42.1

61.4

100.0

0.5

0.8

1.6

6.4

9.3

7.0

28.9

97.1

99.9

100.0

2.1

3.3

5.1

17.0

25.9

0.5

5.6

10.6

78.3

100.0

0.2

0.8

2.0

7.1

33.3

0.1

0.9

18.0

80.5

100.0

1.0

2.9

4.0

7.1

7.1

0.3

19.2

94.9

100.0

100.0

1.0

1.0

1.0

1.0

1.0

100.0

100.0

100.0

100.0

100.0

0.0

0.0

0.0

0.0

0.0

1.4

25.5

66.1

66.1

100.0

46.3

55.6

60.0

69.9

69.9

1.7

88.6

98.0

100.0

100.0

0.0

0.0

0.0

0.0

0.0

100.0

100.0

100.0

100.0

100.0

*Note: No medium-sized banks in our matched data set processed more than 100 XCK forwards.

35

100th

0.6
1,917

Appendix Two (continued)
Part 3: Return Rates for Large Banks (>$2 Billion in Deposits) with More Than 100 Forwards
for at Least One SEC Code
Forward Item Categories

Distribution Moments and Percentiles
Mean 25th Median 75th

All Forwards

Overall Return Rate
1.9
0.8
Total Forwards
3,840,113 11,579
% of all returns by banks with overall return rates at or below
66.9
6.0
specified moments or percentiles of the distribution
by PPD Conditional return rate (%, for 271 banks with > 100
2.0
0.8
PPD forwards)
SEC
% of PPD returns by banks with PPD return rates
Code
21.7
3.7
at or below specified moments or percentiles
ARC Conditional return rate (%, for 58 banks with > 100
0.8
0.3
ARC forwards)
% of ARC returns by banks with ARC return rates
99.8
14.4
at or below specified moments or percentiles
TEL Conditional return rate (%, for 86 banks with > 100
6.3
2.5
TEL forwards)
% of TEL returns by banks with TEL return rates at
77.6
0.4
or below specified moments or percentiles
WEB Conditional return rate (%, for 103 banks with > 100
2.1
0.8
WEB forwards)
% of WEB returns by banks with WEB return rates
72.5
17.2
at or below specified moments or percentiles
POP Conditional return rate (%, for 26 banks with > 100
1.8
1.1
POP forwards)
% of POP returns by banks with POP return rates
38.1
1.5
at or below specified moments or percentiles
POS Conditional return rate (%, for 7 banks with > 100
0.2
0.0
POS forwards)
% of POS returns by banks with POS return rates
4.9
3.0
at or below specified moments or percentiles
MTE Conditional return rate (%, for 5 banks with > 100
0.0
0.0
MTE forwards)
% of MTE returns by banks with MTE return rates
4.8
2.4
at or below specified moments or percentiles
RCK Conditional return rate (%, for 31 banks with > 100
53.1
49.0
RCK forwards)
% of RCK returns by banks with RCK return rates
35.0
7.2
at or below specified moments or percentiles
SHR Conditional return rate (%, for 1 bank with > 100
0.0
0.0
SHR forwards)
% of SHR returns by banks with SHR return rates
98.7
98.7
at or below specified moments or percentiles
XCK Conditional return rate (%, for 4 banks with > 100
30.3
9.1
XCK forwards)
% of XCK returns by banks with XCK return rates
91.7
73.0
at or below specified moments or percentiles

36

95th

100th

1.2
2.0
4.9
34.6
47,400 207,260 8,638,736 309,187,477
18.0

67.0

83.9

100.0

1.2

2.0

5.1

34.8

7.8

21.7

72.4

100.0

0.6

1.1

3.1

3.6

96.9

99.8

100.0

100.0

4.7

7.1

17.6

41.4

4.8

80.7

88.5

100.0

1.4

2.5

6.1

23.0

28.3

74.5

77.7

100.0

1.7

2.5

4.2

4.4

38.1

83.9

98.7

100.0

0.1

0.3

1.0

1.0

3.8

93.2

100.0

100.0

0.0

0.0

0.0

0.0

4.8

50.0

100.0

100.0

53.3

59.7

64.4

85.2

35.0

91.8

99.6

100.0

0.0

0.0

0.0

0.0

98.7

98.7

98.7

100.0

15.1

51.4

83.2

83.2

87.4

91.7

100.0

100.0

Appendix Three: Return Rate Distributions for Additional SEC Codes
(See Table 1 for definitions of these SEC codes)
Mean

25th

Median

75th

95th

100th

2.1

1.1

1.9

2.8

4.4

7.1

79.2

1.5

78.7

90.1

99.9

100.0

1.5
77.4
11.0
8.9
1.2

2.3
67.6
16.3
11.8
2.0

1.5
77.4
11.0
8.9
1.2

1.5
77.6
10.8
8.8
1.3

1.5
77.5
10.7
9.0
1.3

1.5
77.5
10.7
9.0
1.3

0.5

0.0

0.2

1.0

1.5

1.5

31.2

1.1

1.6

97.6

100.0

100.0

4.2
79.3
3.5
12.4
0.5

27.2
55.6
0.0
4.9
12.3

20.0
58.3
7.8
5.2
8.7

1.8
74.3
9.2
14.4
0.2

1.8
74.6
9.2
14.3
0.2

1.8
74.6
9.2
14.3
0.2

0.0

0.0

0.0

0.0

0.0

0.1

15.6

0.0

0.0

0.0

55.0

100.0

4.0
52.0
0.0
4.0
40.0

0.0
16.7
0.0
0.0
0.0

0.0
100.0
0.0
0.0
0.0

0.0
100.0
0.0
0.0
0.0

4.5
68.2
1.1
9.1
17.0

4.4
79.4
0.6
6.3
9.4

POP Returns
Conditional return rate (%, for 43 banks with
POP forwards)
% of POP returns by banks with POP return
rates at or below specified moments or
percentiles
Unauthorized
Return reason percentages, for
NSF
banks with POP return rates at
or below specified moments or Administrative
percentiles (42 banks with
Suspicious
POP returns)
Other

POS Returns
Conditional return rate (%, for 10 banks with
POS forwards)
% of POS returns by banks with POS return
rates at or below specified moments or
percentiles
Unauthorized
Return reason percentages, for
NSF
banks with POS return rates at
or below specified moments or Administrative
percentiles (10 banks with
Suspicious
POS returns)
Other

MTE Returns
Conditional return rate (%, for 165 banks with
MTE forwards)
% of MTE returns by banks with MTE return
rates at or below specified moments or
percentiles
Unauthorized
Return reason percentages, for
NSF
banks with MTE return rates at
or below specified moments or Administrative
percentiles (36 banks with
Suspicious
MTE returns)
Other

37

Appendix Three (continued)
Mean

25th

Median

75th

95th

100th

51.8

45.8

52.8

60.0

72.2

91.9

23.9

1.4

53.1

86.7

99.4

100.0

0.3
82.1
1.9
15.1
0.5

0.4
84.0
1.9
13.3
0.5

0.3
83.3
1.5
14.5
0.5

0.3
81.7
1.8
15.6
0.6

0.3
79.5
2.1
17.4
0.6

0.3
79.4
2.1
17.5
0.6

RCK Returns
Conditional return rate (%, for 79 banks with
RCK forwards)
% of RCK returns by banks with RCK return
rates at or below specified moments or
percentiles
Unauthorized
Return reason percentages, for
NSF
banks with RCK return rates at
or below specified moments or Administrative
percentiles (79 banks with
Suspicious
RCK returns)
Other

SHR Returns
Conditional return rate (%, for 14 banks with
SHR forwards)
% of SHR returns by banks with SHR return
rates at or below specified moments or
percentiles
Unauthorized
Return reason percentages, for
NSF
banks with SHR return rates at
or below specified moments or Administrative
percentiles (2 banks with SHR
Suspicious
returns)
Other

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

100.0

100.0

4.4
67.3
1.7
17.7
8.8

4.1
63.3
0.0
9.5
3.4

4.4
67.3
1.7
17.7
8.8

4.8
71.4
3.4
25.9
14.3

4.2
64.3
3.0
23.8
4.8

4.2
64.3
3.0
23.8
4.8

27.8

10.6

17.9

19.6

83.2

83.2

91.9

85.8

87.6

91.9

100.0

100.0

15.1
14.4
54.3
7.1
9.1

12.9
14.9
55.5
7.4
9.3

13.6
14.8
54.8
7.3
9.5

15.1
14.4
54.3
7.1
9.1

20.6
13.3
50.2
6.6
9.3

20.6
13.3
50.2
6.6
9.3

XCX Returns
Conditional return rate (%, for 5 banks with
XCK forwards)
% of XCK returns by banks with XCK return
rates at or below specified moments or
percentiles
Unauthorized
Return reason percentages, for
NSF
banks with XCK return rates at
or below specified moments or Administrative
percentiles (5 banks with XCK
Suspicious
returns)
Other

38