Symposium 2009
DISCUSSANT’S COMMENTS -
Data Mining Journal Entries for Fraud Detection: A Pilot Study –
R S Debreceny & Glen L Gray
Eckhardt Kriel
I want to congratulate the authors on a very interesting and topical paper.
It is well researched, documented and while I agree with the paper and its conclusions I ask
myself:
Does it go far enough?
 Auditors have been doing this for over 5 years
 There is a massive amount of data - results and experiences
Its perhaps time to ask – “How may frauds have been uncovered and how useful really is this
exercise”? As the Authors state: “There is, however, very little knowledge of the efficacy of
this important class of audit procedures.”
My experiences:
 During 2002 to 2006 I led a team who performed JE analysis for roughly 500 listed
clients in Canada every year.
 Frequent interaction with other areas and firms.
 In that period millions of journal entries were analysed.
 Spent thousands of hours of work.
 Tests complied with SAS 99 and more.
 We found many of the strange anomalies described in paper.
 We found many control exceptions and issues but;
 NO SIGNIFICANT FRAUDS WERE UNCOVERED.
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MOTIVATION - JUSTIFICATION
MOTIVATION - JUSTIFICATION
CHALLENGES IN PROCESS
CHALLENGES
ITS NOT EASY!
Tough challenges that are not mentioned in the paper.
 Accessing and extracting the data.
 Understanding unique client environment and FCP.
 Data Completeness verification.
 Are the appropriate tests being run?
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Our procedures included those mentioned in the paper plus:
Data Completeness
Trial Balance Roll-Up
Data Anomaly Tests
Blank Date Fields
Zero Dollar Items
Blank Account Numbers
Unbalanced Journal Entries
Blank transaction description
Blank Preparer ID
Foreign Currency Adjustments
Unusual Currencies
Key Transaction Tests
Accounts not in the Chart of Accounts
Line items greater than the absolute value
of a dollar threshold
Back Dated Journal Entries greater than the
absolute value of a dollar threshold
Digital Filter Tests
Benford Tests on leading and trailing digits
Round Number testing
Additional Testing Procedures:
• Modified Standard Account/Period/Amount Cross
Tabulation
• Identify any Journal Entry exceeding the average daily
posting amount for that account by x%
• Identify any Journal Entry exceeding the average daily
number of transactions for that account by x%
• Identify Journal Entries with identical dollar amounts
• Account Combination Testing
Debits to Income Accounts and Credits to Expense Accounts
Debits to Liability Accounts and Credits to Income Accounts
Debits to Asset Accounts and Credits to Income Accounts
Debits to Fixed Assets and Credits to Expenses
• Identify Journal Entries with key words in description field –
“professional fees”, “litigation”, “reserve”, etc.
• Identify journal entries passed by unauthorized personnel
etc., etc., etc.
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STANDARD PROCEDURES
Standard Procedures
Data Trending by Source Code – Cross Tabulation of key data fields, cross
comparison of linked items
January
Health Care Client - extract
CASH AND CASH EQUIVALENTS
February
March
April
May
June
Total
($190,471)
($170,670)
($135,814)
($72,919)
($110,761)
($156,791)
($837,426)
$20,218,188
$19,753,469
$19,109,002
$20,801,779
$22,089,963
$25,057,885
$127,030,286
ALLOWANCE FOR UNCOLLECTIBLES
$573,675
$500,398
$440,131
$319,390
$470,985
$702,050
$3,006,629
ALLOWANCE FOR CONTRACTUALS
$874,739
$627,713
$1,222,530
$1,476,653
$1,338,183
$1,219,811
$6,759,629
($4,748,761)
($4,472,640)
($4,850,452)
($5,068,811)
($5,453,126)
($5,319,425)
($29,913,215)
($1)
($1)
$0
($1)
($1)
$0
($4)
PATIENT A/R GROSS RECEIVABLE
INPATIENT DAILY HOSPITAL REVENUE
Total
Comments: Patient revenues of $ 136 million were accrued, resulting in a corresponding increase in AR.
PATIENT A/R GROSS RECEIVABLE
INPATIENT DAILY HOSPITAL REVENUE
30,000,000
6,000,000
25,000,000
5,000,000
20,000,000
4,000,000
15,000,000
3,000,000
10,000,000
2,000,000
5,000,000
1,000,000
0
0
January
February
March
April
May
June
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CROSS TABULATION EXAMPLE
Cross Tabulation Example
SAS 99 details a number of procedures that auditors can follow to respond to the
objective of consideration of fraud in F/S audit. Journal entry testing is one of
these.
I have reservations that, on its own, journal entry testing, is effective. So any paper
or article on the subject must include it as one of a combination of tests.
To detect potential irregularity in financial statements any analysis must be
focused:
It must contemplate fraudulent misstatement and profile its characteristics;
It must search for the characteristics;
It must be broadly based.
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LIMITATIONS
Limitations of Journal Entry Testing
POSSIBLE FUTURE RESEARCH AREAS
Expanding on SAS 99 Paragraphs 28/29/30
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TEXTUAL MINING
Possible future research
Textual Mining
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BENFORD ON COMPANY RESULTS
Possible future research
Benford on Listed
Company Results –
AD Saville1
1. Reference: SAJEMS NS 9 (2006) No 3 341
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Advanced Financial Reporting Analysis – Example MICROSTRATEGY – Application of Different tests
1997 Q1
1997
Q2
1997
Q3
1997
Q4
1998
Q1
1998
Q2
1998
Q3
1998 Q4
1999
Q1
1999
Q2
1999
Q3
1999
Q4
Total Revenue
8,137
11,875
14,751
18,794
19,895
23,790
27,014
35,731
35,784
45,638
54,555
69,352
Gross Profit
5,981
73.5%
9,420
79.3%
11,855
80.4%
15,185
80.8%
16,194
81.4%
19,125
80.4%
21,770
80.6%
29,560
82.7%
28,655
80.1%
37,169
81.4%
44,655
81.9%
57,774
83.3%
(941)
199
498
616
711
1,744
2,685
4,186
2,541
4,573
5,531
5,674
(1,003)
122
486
516
542
942
1,928
2,766
1,859
3,211
3,794
4,044
(994)
3,723
1,306
(310)
(881)
(1,230)
(3,975)
3,538
(1,161)
529
(1,827)
1,252
Operating Earnings
(loss)
Net Income
Net Cash provided
by (used in)
operations
Deferred Revenue
Bal at Dec 31
9,387
11,478
80,000
80,000
70,000
70,000
60,000
60,000
16,782
50,000
50,000
Total Revenue
40,000
40,000
30,000
20,000
Total Revenue
30,000
Gross Profit
20,000
Net Cash provided
by (used in)
operations
10,000
10,000
1999 Q4
1999 Q3
1999 Q2
1999 Q1
1998 Q4
1998 Q3
1998 Q2
1998 Q1
1997 Q4
1997 Q3
1997 Q2
1997 Q1
1997 Q2
1997 Q3
1997 Q4
1998 Q1
1998 Q2
1998 Q3
1998 Q4
1999 Q1
1999 Q2
1999 Q3
1999 Q4
-10,000
1997 Q1
0
0
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ADVANCED FINANCIAL REPORTING ANALYSIS
Possible future research
CONCLUSION
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TOO MUCH INFORMATION ?
Too much Information ?
M Gladwell
Stephen Few’s2 Commentary on Gladwell:
“Modern problems, on the other hand, are not the result of missing or hidden information, Gladwell argues,
but the result, in a sense, of too much information and the complicated challenge of understanding it.
The problems that we face today do not exist because we lack information, but because we don’t understand
it. They can be solved only by developing skills and tools to make sense of information that is often complex.
In other words, the major obstacle to solving modern problems isn’t the lack of information, solved by
acquiring it, but the lack of understanding, solved by analytics.”
2. Visual Business Intelligence
September-21-09
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QUESTIONS
Eckhardt Kriel CA (SA)
E Kriel & Associates Inc.
1148 Forest Trail Place
Oakville
ON L6M 3H7
www.krielassoc.com
www.d3cifer.com
Mobile: +1. 416 451-3919
Direct: +1. 416 451-3919
Email: ekriel@cogeco.ca
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