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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 the Consideration of Fraud

  • 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 the Consideration of Fraud



Brainstorming Quality





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

Why study auditor bstorming
Why Study the Consideration of FraudAuditor 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

Hypotheses the Consideration of Fraud

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?

Method the Consideration of Fraud

  • 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.

Method the Consideration of Fraud

  • 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%).

Method the Consideration of Fraud

Results h1a fr assessment
Results: H1a (FR Assessment) the Consideration of Fraud

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) the Consideration of Fraud

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) the Consideration of Fraud

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 the Consideration of Fraud

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

Conclusion the Consideration of Fraud

  • A lot of variation in brainstorming practices

  • FR factors properly incorporated into FR assessments.

    • Other than DIs, higher quality brainstorming does not improve.

Conclusion the Consideration of Fraud

  • 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?