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or by a nonfraud issue such as the excessive use of These tests will almost
round numbers.
I also concluded that analyzing the four largest
spikes would be enough to detect the material data always bring to light
issues. Furthermore, if the four spikes all identi-
fied irregularities, then the auditor should either patterns and insights that
investigate more spikes or stop and reevaluate the
effectiveness of the client’s internal controls and the
audit risk. In “The First-Two Digits of the Invoice would otherwise have
Amounts With a Threshold Line,” the actionable
and 98. We would not investigate those cases where remained concealed.
spikes, ranked by the Z-statistics, are 50, 11, 10,
the actual proportion is below the Benford’s Law
line because every over essentially causes an under
somewhere else because the actual proportions must transactions that caused the actionable spikes, but
sum to 1.00. they should not do so. In the above example, for
The audit work would thus concentrate on the instance, auditors may want to remove the $50
reasons for the spikes at 50, 11, 10, and 98. To start, deposits, the invoices for $1,153.35, and other
I selected (filtered) the transactions with first-two transactions related to the 10 and 98 spikes from the
digits 50. Then I ran a duplications test to see which data, among others, and then rerun the Benford’s
dollar amounts with 50 occurred most often and Law tests. Doing this will be like playing whack-a-
found in this case there were 6,022 transactions for mole at a carnival because removing those transac-
$50 each. The company’s explanation for this finding tions will simply cause other new spikes and the
was that it required a deposit from new customers, process will repeat itself.
and the $50 payments were refunds to customers If auditors get a result with, say, 10 or more spikes,
that had canceled their service in the first year of its they should reevaluate whether the data should have
term. (As an aside: Experience has shown that there been expected to conform to Benford’s Law in the
are almost always spikes at 50 in journal entry data first place. The first check would be to see whether
because of our tendency to use those first-two digits an error had been made where, for instance, the
for estimates and accruals, and auditors may want to ledger account numbers, foreign exchange rates, or
spend less time investigating a spike at 50.) dates (as a serial number) were analyzed instead of
The next duplications test showed that the the dollar amounts of the journal entries. The second
spike at 11 was mainly caused by 2,263 invoices for check would be to see whether a few types of journal
exactly $1,153.35 from two vendors. The company entries dominate the data — for instance, recurring
confirmed that their usual practice, when items were transactions such as depreciation, bank charges, or
needed six times a day, was to buy in bulk and issue travel per diem amounts. If everything seems in
from inventory, or to be invoiced for batches of order, after reviewing the results for the prior year,
the product. The spike at 98 was entirely caused by auditors might want to reevaluate the effectiveness
1,010 invoices for exactly $988.35 from two vendors
with many instances of multiple purchases per day,
and the spike at 10 was caused by duplications of AICPA RESOURCES
several different amounts all with first-two digits 10.
None of these duplications were related to vendor Articles
fraud, although the possibility of duplicate-paying “Journal Entry Testing Using Excel,” JofA, Nov. 1, 2021
the invoices by mistake was real. “Using Excel and Benford’s Law to Detect Fraud,” JofA, April 1, 2017

CAVEATS
Online resource
Some cautions are needed before using Benford’s
Audit Data Standards
Law-based journal entry testing or general ledger
analytics. Note that Benford’s Law-based analysis is
Podcast episode
not appropriate for all types of journal entries (see
“Lessons Learned From a Multibillion-Dollar Fraud,” JofA, Aug. 22, 2018
the sidebar, “Where to Target Benford’s Law”).
Auditors might be tempted to remove the
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