Fraudulent Financial Reporting: Recent Research

1. Fraudulent Financial Reporting:Recent Research Joe Brazel North Carolina State University 2007 Fiscal Officer Update Seminar December 18, 2007

2. Introduction • 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?

3. 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: • http://papers.ssrn.com/sol3/cf_dev/AbsByAuth.cfm?per_id=340465

4. Using Nonfinancial Measures to Assess Fraud Risk Joe Brazel North Carolina State University Keith Jones George Mason University Mark Zimbelman Brigham Young University

5. Introduction • 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

6. Introduction • 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

7. Motivation • 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

8. Motivation • 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?

9. Example: Del Global Technologies 1997 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 %

10. Hypotheses 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.

11. Sample • 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.

12. Method For each firm we calculate: DIFFt =((Revt – Revt-1) / Rev t-1) – Average: ((NFMt – NFM t-1) / NFM t-1)  where,  Rev =Total revenue (misstated Revt for fraud firms) NFM =Nonfinancial measure t =Initial year of the fraud

13. Method For each firm we ALSO calculate: EMPLOYEE DIFFt =((Revt – Revt-1) / Rev t-1) – ((NFMt – NFM t-1) / NFM t-1)  where,  Rev =Total revenue (misstated Revt for fraud firms) NFM =Number of employees t =Initial year of the fraud

14. Results: H1 Variable   N Mean Difference DIFF Fraud Firms 69 0.29 Competitors 69 0.08 0.21*** EMPLOYEE DIFF Fraud Firms 68 0.28 Competitors 68 0.07 0.21*** Significance Level: *** < .01. Mean Restatement (as a percentage of revenues) = .12

15. 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).

16. Conclusions • 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.

17. 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

18. 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

19. Auditors’ Consideration of Fraud FR Assessment Brainstorming Quality FR Factors FR Responses Plus: Paper examines link between quality of brainstorming and audit effectiveness/fraud detection.

20. 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

21. Hypotheses 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?

22. Method • 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.

23. Method • 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%).

24. Method

25. 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

26. 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

27. 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)

28. 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

29. Conclusion • A lot of variation in brainstorming practices • FR factors properly incorporated into FR assessments. • Other than DIs, higher quality brainstorming does not improve.

30. Conclusion • 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?