DISCUSSANT’S COMMENTS -

Data Mining Journal Entries for Fraud Detection: A Pilot Study – R S Debreceny & Glen L Gray

Symposium 2009

Eckhardt Kriel

2

MO

TIVATION

- JUSTIFICATIO

NMOTIVATION - JUSTIFICATION

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.

3

CHALLEN

GES 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?

4

STANDARD

PROCED

URES

Standard Procedures

Our procedures included those mentioned in the paper plus:

Data CompletenessTrial Balance Roll-Up

Data Anomaly TestsBlank Date FieldsZero Dollar ItemsBlank Account NumbersUnbalanced Journal EntriesBlank transaction descriptionBlank Preparer IDForeign Currency AdjustmentsUnusual Currencies

Key Transaction TestsAccounts not in the Chart of AccountsLine items greater than the absolute value

of a dollar thresholdBack Dated Journal Entries greater than the

absolute value of a dollar thresholdDigital Filter Tests

Benford Tests on leading and trailing digitsRound 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 TestingDebits to Income Accounts and Credits to Expense AccountsDebits to Liability Accounts and Credits to Income AccountsDebits to Asset Accounts and Credits to Income AccountsDebits 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.

5

CROSS TABU

LATION

EXAMPLE

Cross Tabulation Example

Data Trending by Source Code – Cross Tabulation of key data fields, cross comparison of linked items

Health Care Client - extract January February March April May June Total

CASH AND CASH EQUIVALENTS ($190,471) ($170,670) ($135,814) ($72,919) ($110,761) ($156,791) ($837,426)

PATIENT A/R GROSS RECEIVABLE $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

INPATIENT DAILY HOSPITAL REVENUE ($4,748,761) ($4,472,640) ($4,850,452) ($5,068,811) ($5,453,126) ($5,319,425) ($29,913,215)

Total ($1) ($1) $0 ($1) ($1) $0 ($4)

Comments: Patient revenues of $ 136 million were accrued, resulting in a corresponding increase in AR.

January February March April May June 0

5,000,000

10,000,000

15,000,000

20,000,000

25,000,000

30,000,000

0

1,000,000

2,000,000

3,000,000

4,000,000

5,000,000

6,000,000

PATIENT A/R GROSS RECEIVABLE INPATIENT DAILY HOSPITAL REVENUE

6

LIMITATIO

NS

Limitations of Journal Entry Testing

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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POSSIBLE FUTURE RESEARCH AREAS

Expanding on SAS 99 Paragraphs 28/29/30

8

TEXTUAL M

ININ

GPossible future research

Textual Mining

9

BENFO

RD O

N CO

MPAN

Y RESULTS

Possible future research

Benford on Listed Company Results – AD Saville1

1. Reference: SAJEMS NS 9 (2006) No 3 341

10

ADVANCED

FINAN

CIAL REPORTIN

G AN

ALYSISPossible future research

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 5,98173.5%

9,42079.3%

11,85580.4%

15,18580.8%

16,19481.4%

19,12580.4%

21,77080.6%

29,56082.7%

28,65580.1%

37,16981.4%

44,65581.9%

57,77483.3%

Operating Earnings (loss)

(941) 199 498 616 711 1,744 2,685 4,186 2,541 4,573 5,531 5,674

Net Income (1,003) 122 486 516 542 942 1,928 2,766 1,859 3,211 3,794 4,044

Net Cash provided by (used in) operations

(994) 3,723 1,306 (310) (881) (1,230) (3,975) 3,538 (1,161) 529 (1,827) 1,252

Deferred Revenue Bal at Dec 31

9,387 11,478 16,782

1997

Q1

1997

Q2

1997

Q3

1997

Q4

1998

Q1

1998

Q2

1998

Q3

1998

Q4

1999

Q1

1999

Q2

1999

Q3

1999

Q4

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

Total Revenue Gross Profit

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

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

Total Revenue

Net Cash provided by (used in) opera-tions

Advanced Financial Reporting Analysis – Example MICROSTRATEGY – Application of Different tests

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CONCLUSION

12

TOO

MU

CH IN

FORM

ATION

?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 IntelligenceSeptember-21-09

13

QU

ESTION

S

Eckhardt Kriel CA (SA) E Kriel & Associates Inc.1148 Forest Trail PlaceOakvilleON L6M 3H7

www.krielassoc.comwww.d3cifer.com

Mobile:  +1. 416 451-3919Direct:   +1. 416 451-3919 Email: ekriel@cogeco.ca