using financial and nonfinancial measures to improve fraud detection*
DESCRIPTION
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 - PowerPoint PPT PresentationTRANSCRIPT
Using Financial and Nonfinancial Measures to Improve Fraud Detection*
Joseph F. BrazelNorth Carolina State University
The State and Future of Financial FraudNovember 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.
Presentation Overview
Background on Nonfinancial Measures (NFMs)
Research findings
Website
Data from website and future research
2
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
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 facilitiesPatentsDistribution 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, 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?
6
Example: Fraudulent Electronic Component Manufacturer
1997Income: 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
RiskDIFF = Growth in Revenue – Average Growth in
NFMsVariable N Mean EMPLOYEE DIFF
Fraud Firms 110 20% RED FLAG
Competitors 110 4% CAPACITY DIFF
Fraud Firms 50 30% RED FLAG Competitors 50 11% 8
“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
inconsistenciesTool/prompt greatly improves this process
(but ignored under low and medium fraud risk)
9
NFM Prompt
Revenue Expectatio
n
Improving Fraud Detection: Evaluating Auditors’ Reactions to Abnormal
Inconsistencies between Financial and Nonfinancial Measures
FR Assessment
Reliance on NFMs
+
+
-10
Reports from the Field (n = 226 senior level auditors)
0 2 5 10 15 20 25 30 33 40 50 60 65 70 75 80 85 90 95 99 1000
5
10
15
20
25
30
35
40What percent of the time do you use NFMs when
performing analytical procedures?
Num
ber o
f Aud
itor
s
Percentage of time using NFMs when performing A/Ps 11
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
Reports from the FieldConstraints ?
(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
Reports from the FieldImportance 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
“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
SO ……
investors, regulators, auditors, BODs, etc. could use NFMs to better assess fraud risk / improve fraud detection.
16
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
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
19
20
21
22
23
24
Low DIFF Example
25
EDUCATIONAL SERVICES COMPANY 12/31/2007 12/31/2008 Change
Revenues 540,953 623,859 0.153259Total Assets 869,508 1,015,333 0.16771NFMsStudents 53,000 62,000 0.169811Full-time employees 3,960 4,620 0.166667Part-time employees 2,900 3,960 0.365517States with facilities 34 37 0.088235Degree programs 29 33 0.137931Institutions 97 105 0.082474
0.168439DIFF for Revenue -0.01518014DIFF for Assets -0.00072951
High DIFF Example
26
COMPANY X 12/31/2008 12/31/2009 Change
Revenues 1,000,554 1,606,090 0.6052Total Assets 715,296 1,627,678 1.27553NFMsVarieties of X 400 400 0Pounds of X held in futures contracts 2,325,000 2,250,000 -0.03226Places distributed to 10,000 10,000 0US patents 64 66 0.03125International patents 138 146 0.05797Pounds of X sold in millions 64 80 0.25
0.05116DIFF for Revenue 0.5540402DIFF for Assets 1.2243707
Thank you!!!
27