1. Using Financial and Nonfinancial Measures to Improve Fraud Detection* Joseph F. Brazel North Carolina State University The State and Future of Financial Fraud November 3, 2011 * This research was supported by a grant from the FINRA Investor Education Foundation. All results, interpretations and conclusions expressed are those of the authors alone, and do not necessarily represent the views of the FINRA Investor Education Foundation or any of its affiliated companies. No portion of this work may be reproduced, cited, or circulated without the express written permission of the authors.

2. Presentation Overview  Background on Nonfinancial Measures (NFMs)  Research findings  Website  Data from website and future research 2

3. Sponsors  Financial Industry Regulatory Authority (FINRA) Investor Education Foundation  Institute of Internal Auditors Research Foundation  The Institute for Fraud Prevention  Ernst & Young Summer Research Grant  Accounting Firms – for providing access to audit professionals  NCSU Poole COM – for research grants 3

4. 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

5. 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 Associates

6. “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? 6

7. 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

8. Using Nonfinancial Measures to Assess Fraud Risk DIFF = Growth in Revenue – Average Growth in NFMs Variable NMean EMPLOYEE DIFF Fraud Firms 110 20% RED FLAG Competitors 110 4% CAPACITY DIFF Fraud Firms 50 30% RED FLAG Competitors 50 11% 8

9. “Improving Fraud Detection: Evaluating Auditors’ Reactions to Abnormal Inconsistencies between Financial and Nonfinancial Measures” Joe Brazel, Keith Jones, and Doug Prawitt  Key findings: Initial experiment:  Virtually no reaction (5% detected)  Auditors need help detecting abnormal inconsistencies  Tool/prompt greatly improves this process (but ignored under low and medium fraud risk) 9

10. NFM Prompt Revenue Expectation Improving Fraud Detection: Evaluating Auditors’ Reactions to Abnormal Inconsistencies between Financial and Nonfinancial Measures FR Assessment Reliance on NFMs + + - 10

11. Reports from the Field (n = 226 senior level auditors) 11

12. Reports from the Field What percent of the time do you use NFMs when performing A/Ps? Avg = 34% of the time. 13% say never. Things are getting better. To what extent would you test controls/verify data to make sure the nonfinancial measures were accurate? (1= None; 10 = Extensively) Avg = 7.14 12

13. Reports from the Field Constraints ? (n= 89 senior level auditors) (1) Lack of easy availability (58%) (2) Lack of understanding about how NFMs drive company performance (29%) (3) Prior year workpapers do not include analyses of NFMs (18%) 13

14. Reports from the Field Importance of Fraud Red Flags (n = 23 audit 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. 14

15. “Do Nonprofessional Investors React to Fraud Red Flags?” Joe Brazel, Tina Carpenter, Keith Jones, and Jane Thayer.  Key findings: The average NP investor does not react to red flags (accrual and NFM RFs) in the current disclosure environment (not transparent).  Investors do not react to a single, transparent RF. Good(?)  Making multiple, intuitive red flags transparent leads to lower investment levels. Investor thoughts on NFM red flag drives this. 15

16. SO …… investors, regulators, auditors, BODs, etc. could use NFMs to better assess fraud risk / improve fraud detection. 16

17. Tenet Healthcare -- 2009 10-K (page 48) Admissions, Patient Days and Surgeries 2009 2008 Increase (Decrease) Commercial managed care admissions 133,511 140,030 (4.7)% Governmental managed care admissions 118,129 109,450 7.9% Medicare admissions 156,104 161,493 (3.3)% Medicaid admissions 64,405 64,411 — % Uninsured admissions 23,205 24,039 (3.5)% Charity care admissions 10,435 9,284 12.4% Other admissions 13,601 13,906 (2.2)% Total admissions 519,390 522,613 (0.6)% Paying admissions (excludes charity and uninsured) 485,750 489,290 (0.7)% Total government program admissions 338,638 335,354 1.0% Charity admissions and uninsured admissions 33,640 33,323 1.0% Admissions through emergency department 297,911 293,350 1.6% Commercial managed care admissions as a percentage of total admissions 25.7% 26.8% (1.1)% Emergency department admissions as a percentage of total admissions 57.4% 56.1% 1.3% Uninsured admissions as a percentage of total admissions 4.5% 4.6% (0.1)% Charity admissions as a percentage of total admissions 2.0% 1.8% 0.2% Surgeries – inpatient 152,846 154,268 (0.9)% Surgeries – outpatient 209,294 202,195 3.5% Total surgeries 362,140 356,463 1.6% Patient days – total 2,530,528 2,586,187 (2.2)% Adjusted patient days 3,748,764 3,734,085 0.4% Patient days – commercial managed care 535,345 563,018 (4.9)% Average length of stay (days) 4.9 4.9 — Adjusted patient admissions 774,630 759,976 1.9% Number of general hospitals (at end of period) 48 48 — Licensed beds (at end of period) 13,326 13,287 0.3% Average licensed beds 13,309 13,274 0.3% Utilization of licensed beds 52.1% 53.2% (1.1)% 17

18. Problems  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 18

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25. Low DIFF Example 25 EDUCATIONAL SERVICES COMPANY 12/31/200712/31/2008Change Revenues540,953623,8590.153259 Total Assets869,5081,015,3330.16771 NFMs Students53,00062,0000.169811 Full-time employees3,9604,6200.166667 Part-time employees2,9003,9600.365517 States with facilities34370.088235 Degree programs29330.137931 Institutions971050.082474 0.168439 DIFF for Revenue-0.01518014 DIFF for Assets-0.00072951

26. High DIFF Example 26 COMPANY X 12/31/200812/31/2009Change Revenues1,000,5541,606,0900.6052 Total Assets715,2961,627,6781.27553 NFMs Varieties of X400 0 Pounds of X held in futures contracts2,325,0002,250,000-0.03226 Places distributed to10,000 0 US patents64660.03125 International patents1381460.05797 Pounds of X sold in millions64800.25 0.05116 DIFF for Revenue0.5540402 DIFF for Assets1.2243707

27. Thank you!!! 27