Use of Non-financial
Measures to Detect
Fraudulent Financial
Reporting: Evidence from
Recent Research
Joseph F. Brazel
North 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
2
Caveats
 External audit focus
 10,000 foot level / Highlights Tour
 All these papers are available at ssrn.com
3
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 facilities
Patents
Distribution 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,
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Computer Associates
“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
1997
Income: 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%
7
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%
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“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
inconsistencies
Tool/prompt greatly improves this process
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(but ignored under low and medium fraud risk)
Auditors’ Reactions to Abnormal Inconsistencies
between Financial and Nonfinancial Measures:
The Interactive Effects of Fraud Risk Assessment
FR
Assessment
+
NFM
Prompt
Revenue
Reliance
+ on NFMs - Expectation
10
Reports from the Field 2009
(n = 226 senior level auditors)
What percent of the time do you use NFMs when
performing A/Ps?
40
Number of Auditors
35
30
25
20
15
10
5
0
0
2
5
10
15
20
25
30
33
40
50
60
65
70
75
80
85
90
Percentage of time using NFMs when performing A/Ps
95
99
100
11
Reports from the Field 2013
(n = 94 senior level auditors)
What percent of the time do you use NFMs when
performing A/Ps?
12
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??????
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“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
ABOVE
3,0
ID
MISSTATE
2,5
2,0
EVAL
1,5
1,0
NO ID
MISSTATE
0,5
MET
0,0
Low audit
committee support
High audit
committee support
What About CONSULTATION as a
SOLUTION?
ABOVE
3,5
3,0
ID
MISSTATE
2,5
EVAL2,0
1,5
NO ID
MISSTATE
1,0
0,5
MET 0,0
NO CONSULTATION KEEP INFORMED
GET APPROVAL
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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22
DIFF = Change in Revenue - Average Change in NFMs
More
1
0.95
0.9
0.85
0.8
0.75
0.7
0.65
0.6
0.55
0.5
0.45
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
-0.05
-0.1
-0.15
-0.2
-0.25
-0.3
-0.35
-0.4
-0.45
-0.5
-0.55
-0.6
-0.65
-0.7
-0.75
-0.8
-0.85
300
-0.9
-0.95
-1
Frequency
Sample from the Website
Revenue DIFF Frequency Distribution N = 1,585 Observations (Company/Years)
250
200
150
100
50
0
Sample from the Website
300
Revenue DIFF Frequency Distribution N = 1,585 Observations (Company/Years)
3X as likely
to have an
SEC
investigation
and have a
Class Action
Lawsuit
200
150
100
50
DIFF = Change in Revenue - Average Change in NFMs
More
1
0.95
0.9
0.8
0.85
0.75
0.7
0.65
0.6
0.55
0.5
0.45
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0
0.05
-0.05
-0.1
-0.15
-0.2
-0.3
-0.25
-0.35
-0.4
-0.45
-0.5
-0.55
-0.6
-0.65
-0.7
-0.8
-0.75
-0.85
-0.9
-0.95
0
-1
Frequency
250
Sample from the Website
300
Revenue DIFF Frequency Distribution N = 1,585 Observations (Company/Years)
Auditors with
less tenure
and
industry
expertise
200
150
100
50
DIFF = Change in Revenue - Average Change in NFMs
More
1
0.95
0.9
0.8
0.85
0.75
0.7
0.65
0.6
0.55
0.5
0.45
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0
0.05
-0.05
-0.1
-0.15
-0.2
-0.3
-0.25
-0.35
-0.4
-0.45
-0.5
-0.55
-0.6
-0.65
-0.7
-0.8
-0.75
-0.85
-0.9
-0.95
0
-1
Frequency
250
Sample from the Website
300
Revenue DIFF Frequency Distribution N = 1,585 Observations (Company/Years)
Audit
Committee
Chairs with
greater
industry
expertise
Authority
Story?
200
150
100
50
DIFF = Change in Revenue - Average Change in NFMs
More
1
0.95
0.9
0.8
0.85
0.75
0.7
0.65
0.6
0.55
0.5
0.45
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0
0.05
-0.05
-0.1
-0.15
-0.2
-0.3
-0.25
-0.35
-0.4
-0.45
-0.5
-0.55
-0.6
-0.65
-0.7
-0.8
-0.75
-0.85
-0.9
-0.95
0
-1
Frequency
250
Sample from the Website
300
Revenue DIFF Frequency Distribution N = 1,585 Observations (Company/Years)
Italian CFOs
get nervous!
200
150
100
50
DIFF = Change in Revenue - Average Change in NFMs
More
1
0.95
0.9
0.8
0.85
0.75
0.7
0.65
0.6
0.55
0.5
0.45
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0
0.05
-0.05
-0.1
-0.15
-0.2
-0.3
-0.25
-0.35
-0.4
-0.45
-0.5
-0.55
-0.6
-0.65
-0.7
-0.8
-0.75
-0.85
-0.9
-0.95
0
-1
Frequency
250
Sample from the Website
300
Revenue DIFF Frequency Distribution N = 1,585 Observations (Company/Years)
Management
less likely to
issue
earnings
forecast
200
150
Management
less likely to
issue
earnings
forecast
100
50
DIFF = Change in Revenue - Average Change in NFMs
More
1
0.95
0.9
0.8
0.85
0.75
0.7
0.65
0.6
0.55
0.5
0.45
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0
0.05
-0.05
-0.1
-0.15
-0.2
-0.3
-0.25
-0.35
-0.4
-0.45
-0.5
-0.55
-0.6
-0.65
-0.7
-0.8
-0.75
-0.85
-0.9
-0.95
0
-1
Frequency
250
Sample from the Website
300
Revenue DIFF Frequency Distribution N = 1,585 Observations (Company/Years)
Management
forecast
errors
increase
200
150
Management
forecast
errors
increase
100
50
DIFF = Change in Revenue - Average Change in NFMs
More
1
0.95
0.9
0.8
0.85
0.75
0.7
0.65
0.6
0.55
0.5
0.45
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0
0.05
-0.05
-0.1
-0.15
-0.2
-0.3
-0.25
-0.35
-0.4
-0.45
-0.5
-0.55
-0.6
-0.65
-0.7
-0.8
-0.75
-0.85
-0.9
-0.95
0
-1
Frequency
250
Thank you!!!
Questions?
Comments?
ssrn.com
jfbrazel@ncsu.edu
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