using financial and nonfinancial measures to improve fraud detection*

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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.

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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 Presentation

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Page 1: Using Financial and Nonfinancial Measures to Improve Fraud Detection*

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.

Page 2: Using Financial and Nonfinancial Measures to Improve Fraud Detection*

Presentation Overview

Background on Nonfinancial Measures (NFMs)

Research findings

Website

Data from website and future research

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Page 3: Using Financial and Nonfinancial Measures to Improve Fraud Detection*

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

Page 4: Using Financial and Nonfinancial Measures to Improve Fraud Detection*

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

Page 5: Using Financial and Nonfinancial Measures to Improve Fraud Detection*

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

Page 6: Using Financial and Nonfinancial Measures to Improve Fraud Detection*

“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?

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Page 7: Using Financial and Nonfinancial Measures to Improve Fraud Detection*

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%

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Page 8: Using Financial and Nonfinancial Measures to Improve Fraud Detection*

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

Page 9: Using Financial and Nonfinancial Measures to Improve Fraud Detection*

“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)

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Page 10: Using Financial and Nonfinancial Measures to Improve Fraud Detection*

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

Page 11: Using Financial and Nonfinancial Measures to Improve Fraud Detection*

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

Page 12: Using Financial and Nonfinancial Measures to Improve Fraud Detection*

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

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Page 13: Using Financial and Nonfinancial Measures to Improve Fraud Detection*

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%)

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Page 14: Using Financial and Nonfinancial Measures to Improve Fraud Detection*

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

Page 15: Using Financial and Nonfinancial Measures to Improve Fraud Detection*

“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

Page 16: Using Financial and Nonfinancial Measures to Improve Fraud Detection*

SO ……

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

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Page 17: Using Financial and Nonfinancial Measures to Improve Fraud Detection*

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)% 

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Page 18: Using Financial and Nonfinancial Measures to Improve Fraud Detection*

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

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Low DIFF Example

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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

Page 26: Using Financial and Nonfinancial Measures to Improve Fraud Detection*

High DIFF Example

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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

Page 27: Using Financial and Nonfinancial Measures to Improve Fraud Detection*

Thank you!!!

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