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Fraudulent Financial Reporting: Recent Research. Joe Brazel North Carolina State University 2007 Fiscal Officer Update Seminar December 18, 2007. Introduction. What is academic accounting research? Explains how the world works (examines causal relationships)

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fraudulent financial reporting recent research

Fraudulent Financial Reporting:Recent Research

Joe Brazel

North Carolina State University

2007 Fiscal Officer Update Seminar

December 18, 2007

  • What is academic accounting research?
    • Explains how the world works (examines causal relationships)
    • Typically Empirical (sample → population)
    • Scientific method
      • What has been done? What don’t we know (RQ)? Why do we care?
      • Hypotheses development (X → Y)
      • Method: Archival, Experimental, Survey
      • Results: Statistics (e.g., regression), test relationships
      • Conclusions: What did we learn?
fraud research
Fraud Research
  • Under-researched, Why?
    • Lack of good data(Levitt and Dubner 2005)
    • Sensitive topic – corporations / audit firms
  • Today’s topics/RQs related to fraud (two studies)

1. Should nonfinancial measures (NFMs) be used as a benchmark for financial data and can this analysis aid in FR assessment?

2. Does higher quality brainstorming improve the fraud audit process?

    • Caveats:External audit perspective, not a governmental accounting/auditing expert, fraudulent financial reporting vs. misappropriation, study 1 – public company frauds, study 2 – 20% of data governmental audits
  • Link to papers:
using nonfinancial measures to assess fraud risk

Using Nonfinancial Measures to Assess Fraud Risk

Joe Brazel

North Carolina State University

Keith Jones

George Mason University

Mark Zimbelman

Brigham Young University

  • What are NFMs?
  • Measures of business activity, sometimes managerial accounting data, often in 10K but not in financial statements, not audited, produced internally or externally, industry specific (can obtain for competitors), SEC: explain your financial results
  • NFMs may be less vulnerable to manipulation and/or are more easily verified than financial data (Bell et al. 2005; PCAOB 2007) – Better benchmark for A/P? (PCAOB 2004)
    • Independent sources or from outside accounting / finance
    • Not estimates
    • Collusion may be required
  • Examples from our study:
    • Number of employees (Compustat)
    • Number of retail outlets
    • Number of patient visits
    • Square footage of production facilities
    • Number of products
    • Number of patents or trademarks
  • Teaching / Guidance(SAS 56 and SAS 99)
    • NFMs → Analytical Procedures → FR Assessment
  • Prior research, practice experience, and current discussions: Auditors tend not to use NFMs. Why?
    • Time constraints / budgetary pressures(Houston 1999)
    • Over-reliance on prior year workpapers that do not include analyses of NFMs(Wright 1988; Brazel et al. 2004)
    • Lack of creative thinking / industry knowledge?
  • Popular Press:HealthSouth, Delphi
  • There is evidence that NFMs are correlated with financial statement data(e.g., Ittner and Larcker 1998; Lundholm and McVay 2006)
  • PCAOB (2004) –Should auditors be required to compare audited financial information with NFMs?
  • Can NFMs increase audit effectiveness or help auditors assess fraud risk?
  • Reasonableness check:Revenue Growth = NFM Growth?
example del global technologies
Example: Del Global Technologies


Income:Overstated $3.7 million.

Revenue: 25 % from PY.

Employees: 6 % (440 to 412)

Distribution Dealers: 38% (400 to 250)

Fischer Imaging Corp:

Revenue: 27 %

Employees: 20 %

Distribution Dealers: 24 %


H1:Fraud firms will have greater differences between their percent change in revenue growth and percent change in NFMs than their non-fraud competitors.

H2:Including an independent variable that compares change in revenue growth and change in NFMs adds to the power of a fraud risk assessment model comprised of factors that have previously been associated with fraudulent financial reporting.

  • Period:1993-2002
  • 69 Fraud Firms (from AAER’s) –Revenue only
  • 69 Competitors(from Hoover’s Online)
  • Requirement:Needed the same type of NFM for fraud firm and competitor AND need that NFM for year of fraud and year before.

For each firm we calculate:

DIFFt =((Revt – Revt-1) / Rev t-1) –

Average: ((NFMt – NFM t-1) / NFM t-1) 


Rev =Total revenue (misstated Revt for fraud firms)

NFM =Nonfinancial measure

t =Initial year of the fraud


For each firm we ALSO calculate:

EMPLOYEE DIFFt =((Revt – Revt-1) / Rev t-1) –

((NFMt – NFM t-1) / NFM t-1) 


Rev =Total revenue (misstated Revt for fraud firms)

NFM =Number of employees

t =Initial year of the fraud

results h1
Results: H1

Variable   N Mean Difference


Fraud Firms 69 0.29

Competitors 69 0.08 0.21***


Fraud Firms 68 0.28

Competitors 68 0.07 0.21***

Significance Level: *** < .01.

Mean Restatement (as a percentage of revenues) = .12

results h2
Results: H2

Logistic Regression:

P(FRAUDt) = 0 + 1Difft + 2Incentive Factorst + 3Opportunity Factorst + 4Suspicious Accounting Factorst + 5Other Controls

Incentives –Market (e.g., Need for financing), Debt (e.g., Altman Z), Age of firm, Prior performance, M&A

Opportunities –Big N, Insiders on BOD, CEO = COB

Suspicious Accounting –Total accruals, Special items, Revenue Growth

Other Controls –Size, Negative Change in NFM

Both DIFF and EMPLOYEE DIFF are positive and significant (p < .05).

  • Take off the financial statement blinders
  • Use NFMs to evaluate F/S data (planning and substantive) –descriptive benchmarks, change the nature of testing
  • Anecdotal stories and PCAOB claims confirmed by empirical evidence
  • Auditor has access to more client NFMs than we did (publicly available, time lapse, industry databases of firm)
  • As Diff increases:Ask pointed questions/corroborate mgt explanations, increase FR assessment, tipping point, devote more resources/increase scope
  • FINRA Grant: Experimental studies, investor tool.
a field investigation of auditors use of brainstorming in the consideration of fraud

A Field Investigation of Auditors’ Use of Brainstorming in the Consideration of Fraud

Joe Brazel

NC State University

Tina Carpenter

University of Georgia

Greg Jenkins

Virginia Tech

research objectives
Research Objectives
  • Using a field survey of recently completed audits:
    • Examine application of SAS No. 99
    • Describe how audit teams are conducting fraud brainstorming sessions
    • Determine whether the quality of these sessions affects the consideration of fraud
auditors consideration of fraud
Auditors’ Consideration of Fraud



Brainstorming Quality





Plus: Paper examines link between quality of brainstorming and audit effectiveness/fraud detection.

why study auditor bstorming
Why Study Auditor Bstorming?
  • Prior psych literature:Mixed, with students, emphasis on quantity vs. quality of ideas, less crucial issues, lack of hierarchy
  • Prior accounting literature:Experimental, on/off switch, lack of partner, focused on FR assessments
  • Empirically assess PCAOB bstorming concerns with data from practice: Ramifications?
  • Firms wanted to know how others were bstorming

H1a:Fraud risk factors are positively related to fraud risk assessments.

H1b:Fraud risk factors become more positively related to fraud risk assessments as the quality of fraud brainstorming sessions increase.

H2:Fraud risk assessments become more positively related to fraud risk responses as the quality of fraud brainstorming sessions increase.

Why no direct effect of FR on Response?

  • 179 recently completed audits (online survey): All B4 and National Firm, variety of industries
    • 56 partners
    • 2 directors
    • 60 senior managers
    • 61 managers
  • FR Factors:Market and Debt Incentives, Opportunities, Attitudes
  • FR Assessment:Scale (1-10)
  • FR Response:Nature, Staffing, Timing, Extent of testing
  • Control Variables: Size, Industry, Team Expertise, Firm, Fraud Experience, Fraud Training, etc.
  • Quality of Brainstorming (from literature)
    • 21 Item Measure (0=low, 1=high; score: 0-21)
    • Categories:
      • Attendance and Communication
        • Partner/CFE led; All attended, Above mean participation = 1 for each
      • Brainstorming Format
        • Agenda used; pre or early planning = 1 for each
      • Engagement Team Effort
        • Above mean total time spent; more than one; ID risks prior = 1 for each
    • Interesting findings:Partner/CFE led (60%),not all members (27%), fraud specialist (31%), IT/Tax (69/63%), hierarchical participation, use of checklist (72%), held late (35%), no wrap-up in PY (84%), average total time (1.5 hours), more than one session (50%).
results h1a fr assessment
Results: H1a (FR Assessment)

Independent Variable Coeff. tp

Market incentive .150 1.70 .046

Debt incentive .109 1.44 .076

Opportunity .218 2.55 .006

Rationalization .152 1.88 .031

Client size -.153 1.88 .062

Engag. team expertise .164 2.01 .046

Fraud training .138 1.90 .060

results h1b fr assessment
Results: H1b (FR Assessment)

Independent Variable Coeff. tp

MIxSession Quality -.064 .20 .844

DIxSession Quality .511 1.88 .031

OppxSession Quality -.111 .35 .725

RatxSession Quality -.349 1.22 .226

Client size -.204 2.45 .015

Engag. team expertise .157 1.93 .055

Fraud training .132 1.77 .079

High tech/Comm Industry .155 1.67 .097

results h2 fr response
Results: H2 (FR Response)

Nature: FR (pos ns), FRxSessionQuality (pos ns)

Staffing: FR(neg ns), FRxSessionQuality(pos sig)

Timing: FR (neg ns), FRxSessionQuality (pos sig)

Extent: FR (pos ns), FRxSessionQuality (pos sig)

bstorming and effectiveness
Bstorming and Effectiveness

For 43/179: fraud detected during engagement -but may be immaterial/misappropriation of assets

  • Did bstorming aid in the detection? 13/43 – good or bad?
  • Positively correlated with bstorming quality: Effectiveness of fraud audit, confidence in fraud process, and AC satisfaction with fraud work
  • A lot of variation in brainstorming practices
  • FR factors properly incorporated into FR assessments.
    • Other than DIs, higher quality brainstorming does not improve.
  • The quality of brainstorming does positively affect the link between FR assessments and responses.
    • Still do not find link with nature of responses.
    • So, even in cases of high quality brainstorming, auditors appear to react to higher FR assessments with spending more time performing PY tests, at different times, and with more qualified professionals.
    • Why?SALY, lack of education/training/guidance, lack of creativity, more/better use of CFEs?