use of non-financial measures to detect fraudulent financial reporting: evidence from recent...
TRANSCRIPT
Use of Non-financial Measures to Detect Fraudulent Financial
Reporting: Evidence from Recent Research
Joseph F. BrazelNorth Carolina State University
Raleigh-Durham Chapter of The Institute of Internal Auditors
January 13, 2015
Sponsors Institute of Internal Auditors
Research Foundation
Financial Industry Regulatory Authority (FINRA) Investor Education Foundation
The Institute for Fraud Prevention
IAASB
CohnReznick, KPMG, and Ernst & Young
NCSU Poole COM
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Caveats External audit focus
10,000 foot level / Highlights Tour
All these papers are available at ssrn.com
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Background Financial Measures = Revenue, Earnings, Total Assets,
etc.
What are “Nonfinancial Measures” (NFMs)?
Examples from Brazel, Jones, and Zimbelman (2009)Number of:
Employees Retail outlets Patient visits Production facilitiesPatentsDistribution Centers
Square footage of production facilities 4
Background NFMs are measures of business activity:
Often in 10-K (Part 1 and MD&A) – in the same 10-K filing as fraudulent financial statements
Produced internally and externally (e.g., customer satisfaction)
“Explains” financial results, current push for more disclosure
Correlated with financial statement data
Easy to verify / hard to conceal manipulation
Good benchmark for financial statements
“Fraud” = Fraudulent Financial Reporting, “cooking the books”Enron, WorldCom, Xerox, The North Face, Rite Aid,
Computer Associates5
“Using Nonfinancial Measures to Assess Fraud Risk,” Joe Brazel, Keith Jones, and Mark Zimbelman. Journal of Accounting Research, December 2009, Volume 47, Issue 5, pp. 1135-1166.
Research Question
If NFMs serve as a good benchmark for the financial statements, do fraudulent firms exhibit NFM RED FLAGS?
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Example: Fraudulent Electronic Component Manufacturer
1997Income: Overstated $3.7 million.Revenue: 25% from Prior Year.Employees: 6% (440 to 412)Distribution Dealers: 38% (400 to 250)
Non-fraud Electronic Component Manufacturer:
Revenue: 27%Employees: 20%Distribution Dealers: 7%
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Using Nonfinancial Measures to Assess Fraud
Risk
DIFF = Growth in Revenue – Average Growth in NFMs
Variable N Mean
EMPLOYEE DIFF Fraud Firms 110 20% RED
FLAG Competitors 110 4%
CAPACITY DIFF Fraud Firms 50 30% RED
FLAG Competitors 50 11% 8
“Auditors’ Reactions to Abnormal Inconsistencies between Financial and Nonfinancial Measures: The Interactive Effects of Fraud Risk Assessment,” Joe Brazel, Keith Jones, and Doug Prawitt. Behavioral Research in Accounting, Spring 2014, Volume 26, Issue 1, pp. 131-156.
Key findings:Virtually no reaction to NFM red flag
without “help” (only 5% detected) Auditors need help detecting abnormal
inconsistenciesTool/prompt greatly improves this process
(but ignored under low and medium fraud risk)
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NFM Prompt
Revenue Expectatio
n
Auditors’ Reactions to Abnormal Inconsistencies between Financial and Nonfinancial Measures: The Interactive
Effects of Fraud Risk Assessment FR
Assessment
Reliance on NFMs
+
+
-
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Reports from the Field 2009 (n = 226 senior level auditors)
0 2 5 10 15 20 25 30 33 40 50 60 65 70 75 80 85 90 95 99 1000
5
10
15
20
25
30
35
40What percent of the time do you use NFMs when
performing A/Ps?
Nu
mb
er
of
Au
dit
ors
Percentage of time using NFMs when performing A/Ps 11
Reports from the Field 2013 (n = 94 senior level auditors)
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What percent of the time do you use NFMs when performing A/Ps?
Reports from the Field
Importance of Fraud Red Flags (n = 23 managers and partners)
12 common red flags investigated
(1) MW over revenue recognition(2) NFM red flag(3) Significant EBC for Mgt(4) Difficult discussions with Mgt over audit adjustments(5) CFO resignation
Important that staff bring NFM red flag to attention of engagement management, but may not always be the
case.WHY?????? 13
“Hindsight Bias and Professional Skepticism,” Joe Brazel, Scott Jackson, Tammie Schaefer, and Bryan Stewart, working paper
To detect the NFM red flag you must be SKEPTICAL: search for inconsistent evidence from a non-traditional evidence source, but also more COSTS (budget, mgt relations)!
Research Question
Are the audit firms currently rewarding appropriate skeptical behavior, regardless of the outcome? (Recent PCAOB synthesis paper) 14
Auditor Experiment Experiment with 75 practicing audit seniors.
Role: Evaluator of a subordinate performing a substantive A/P related to a division’s revenue balance.
All Subordinates:SKEPTICAL JUDGMENT: Decided to use NFMs in CY
(employees, production space). NFMs not consistent with revenue. Revenue consistent with other sources used in PY. So PS led to IDing NFM Red Flag.
SKEPTICAL ACT: Investigated the NFM red flag, outsourcing overseas (Brazel et al. 2009)
Auditor Experiment
For half, subordinate investigated red flag and IDed a MM in overseas operation.
For other half, subordinate investigated red flag and DID NOT ID MM in overseas operation.
All subordinates encountered same costs of PS: over-budget and upset management.
Also, manipulated AC Support (high vs. low): fees and mgt.
Auditor Experiment KEY DV: Evaluation of subordinate (-5, 0, +5)
-5 = Below Expectations
0 = Met Expectations
+5 = Above Expectations
Results - Experiment
Low audit committee support
High audit committee support
0.0
0.5
1.0
1.5
2.0
2.5
3.0
EVAL
ID MISSTATE
NO ID MISSTATE
ABOVE
MET
What About CONSULTATION as a SOLUTION?
NO CONSUL-TATION
KEEP INFORMED GET APPROVAL0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
NO ID MIS-STATE
ID MISSTATE
ABOVE
EVAL
MET
THE GOOD NEWS and the NEXT RESEARCH QUESTION
NFM Problems for OUTSIDERS
F/S comparative, NFM disclosures for CY only
NFM data scattered in 50-100 page 10-K
What specific NFMs should I look for? What are the benchmarks for my investment/client and industry?
So, using NFMs is too hard and too time consuming (5-6 hours to hand collect per company)
Only limited evidence, in very specific industries (pharma), of PROFESSIONAL investors using NFMs.
FINRA grants → Create a tool to solve problems based on research
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-1 -0.9
-0.8
-0.7
-0.6
-0.5
-0.4
-0.3
-0.2
-0.1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
0
50
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200
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300
Fre
qu
en
cy
Revenue DIFF Frequency Distribution N = 1,585 Observations (Company/Years)
Sample from the Website
DIFF = Change in Revenue - Average Change in NFMs
-1 -0.9
-0.8
-0.7
-0.6
-0.5
-0.4
-0.3
-0.2
-0.1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
0
50
100
150
200
250
300
Fre
qu
en
cy
Revenue DIFF Frequency Distribution N = 1,585 Observations (Company/Years)
3X as likely to have an SEC inves-tigation and have a Class Ac-tion Law-suit
Sample from the Website
DIFF = Change in Revenue - Average Change in NFMs
-1 -0.9
-0.8
-0.7
-0.6
-0.5
-0.4
-0.3
-0.2
-0.1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
0
50
100
150
200
250
300
Fre
qu
en
cy
Revenue DIFF Frequency Distribution N = 1,585 Observations (Company/Years)
Auditors withless ten-ure and industry expertise
Sample from the Website
DIFF = Change in Revenue - Average Change in NFMs
-1 -0.9
-0.8
-0.7
-0.6
-0.5
-0.4
-0.3
-0.2
-0.1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
0
50
100
150
200
250
300
Fre
qu
en
cy
Revenue DIFF Frequency Distribution N = 1,585 Observations (Company/Years)
Audit Committee Chairs with greater industry expertiseAuthority Story?
Sample from the Website
DIFF = Change in Revenue - Average Change in NFMs
-1 -0.9
-0.8
-0.7
-0.6
-0.5
-0.4
-0.3
-0.2
-0.1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
0
50
100
150
200
250
300
Fre
qu
en
cy
Revenue DIFF Frequency Distribution N = 1,585 Observations (Company/Years)
Italian CFOs get nervous!
Sample from the Website
DIFF = Change in Revenue - Average Change in NFMs
-1 -0.9
-0.8
-0.7
-0.6
-0.5
-0.4
-0.3
-0.2
-0.1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
0
50
100
150
200
250
300
Fre
qu
en
cy
Revenue DIFF Frequency Distribution N = 1,585 Observations (Company/Years)
Manage-ment less likely to is-sue earn-ings fore-cast
Manage-ment less likely to is-sue earn-ings fore-cast
Sample from the Website
DIFF = Change in Revenue - Average Change in NFMs
-1 -0.9
-0.8
-0.7
-0.6
-0.5
-0.4
-0.3
-0.2
-0.1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
0
50
100
150
200
250
300
Fre
qu
en
cy
Revenue DIFF Frequency Distribution N = 1,585 Observations (Company/Years)
Manage-ment fore-cast errors in-crease
Manage-ment fore-cast errors in-crease
Sample from the Website
DIFF = Change in Revenue - Average Change in NFMs
