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©2016 CASE STUDIES IN ADVANCED FINANCIAL STATEMENT ANALYSIS Different analytical techniques are used in different types of organizations to determine the possibility of financial statement fraud, embezzlement or other types of fraudulent schemes that create unusual relationships in financial statements. This session uses case studies to walk you through the process of analyzing data and, more importantly, how to interpret the results of the tests. In addition, the case studies show how to use visual techniques to easily explain the testing and its results. Key concepts include the Beneish M-Score Model, Z-Score analysis, CRO and cash flow analysis, Sloan's Accruals, Dechow-Dichev Accrual Quality, Jones Nondiscretionary Accruals, Piotroski's F-Score Analysis, Lev-Thiagarajan's 12 Signals, and an overview of Benford's Law. PAMELA MANTONE, CFE, CPA, MAFF, CITP Director Forensic Investigations Elliott Davis Decosimo Pam Mantone specializes in litigation support services with emphasis on forensic accounting and fraud examinations. She has performed forensic and fraud auditing services for organizations, including the gathering of forensic evidence and testifying to findings. Mantone also provides consulting services regarding implementation of fraud prevention and fraud detection internal control systems. Her experience includes conducting and supervising audits of local banks, credit unions, local nonprofit organizations, and HUD audits. She manages and performs external and internal audits of financial institutions. Her book, Using Analytics to Detect Possible Fraud: Tools and Techniques, was published in 2013 and provides a common source of analytical techniques used in forensic accounting investigations. “Association of Certified Fraud Examiners,” “Certified Fraud Examiner,” “CFE,” “ACFE,” and the ACFE Logo are trademarks owned by the Association of Certified Fraud Examiners, Inc. The contents of this paper may not be transmitted, republished, modified, reproduced, distributed, copied, or sold without the prior consent of the author.

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Page 1: CASE STUDIES IN ADVANCED FINANCIAL STATEMENT ANALYSISvirtualconference.acfe.com/materials/9F-Pamela-Mantone.pdf · case studies in advanced financial statement analysis ... piotroski's

©2016

CASE STUDIES IN ADVANCED FINANCIAL STATEMENT ANALYSIS

Different analytical techniques are used in different types of organizations to determine the

possibility of financial statement fraud, embezzlement or other types of fraudulent schemes that

create unusual relationships in financial statements. This session uses case studies to walk you

through the process of analyzing data and, more importantly, how to interpret the results of the

tests. In addition, the case studies show how to use visual techniques to easily explain the testing

and its results. Key concepts include the Beneish M-Score Model, Z-Score analysis, CRO and

cash flow analysis, Sloan's Accruals, Dechow-Dichev Accrual Quality, Jones Nondiscretionary

Accruals, Piotroski's F-Score Analysis, Lev-Thiagarajan's 12 Signals, and an overview of

Benford's Law.

PAMELA MANTONE, CFE, CPA, MAFF, CITP

Director – Forensic Investigations

Elliott Davis Decosimo

Pam Mantone specializes in litigation support services with emphasis on forensic accounting

and fraud examinations. She has performed forensic and fraud auditing services for

organizations, including the gathering of forensic evidence and testifying to findings. Mantone

also provides consulting services regarding implementation of fraud prevention and fraud

detection internal control systems. Her experience includes conducting and supervising audits of

local banks, credit unions, local nonprofit organizations, and HUD audits. She manages and

performs external and internal audits of financial institutions. Her book, Using Analytics to

Detect Possible Fraud: Tools and Techniques, was published in 2013 and provides a common

source of analytical techniques used in forensic accounting investigations.

“Association of Certified Fraud Examiners,” “Certified Fraud Examiner,” “CFE,” “ACFE,” and the

ACFE Logo are trademarks owned by the Association of Certified Fraud Examiners, Inc. The contents of

this paper may not be transmitted, republished, modified, reproduced, distributed, copied, or sold without

the prior consent of the author.

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CASE STUDIES IN ADVANCED FINANCIAL STATEMENT ANALYSIS

27th

Annual ACFE Global Fraud Conference ©2016 1

NOTES Case Studies

Advanced financial analysis of financial statements

provides a wealth of information for those who want to

know that the financial information is reasonable and free

from material errors, whether it is a CFO or CEO

reviewing their own financial statements or a forensic

accountant trying to find whether the financial statements

have been misstated. While the tools and techniques

covered in the presentation are not sufficient by

themselves for prosecution, they do provide a means of

finding areas that have unusual variations and require

further investigation. They are valid for any type of

organization, whether it is a manufacturing or wholesale

distribution company, nonprofit organizations,

governmental entities, or service organizations. The tools

and techniques covered in the presentation include the

following:

Cash Flow and the Net Income Ratio

Operating Performance Ratio

Vertical Analysis

Lev-Thiagarajan’s 12 Signals

Piotroski’s F-Score Model

Beneish M-Score Model

Dechow-Dichev Accrual Quality

Sloan’s Accruals

Jones Nondiscretionary Accruals

Overview of Benford’s Law

Z-score Analysis

By using these tools and techniques, the forensic

accountant will be building a road map of areas that

require additional investigative work by defining areas of

higher risk and the possibility of fraudulent activity. The

calculations of the various techniques from the case

studies are produced in a visual form using various types

of graphs rather than the use of numbers. Visual

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CASE STUDIES IN ADVANCED FINANCIAL STATEMENT ANALYSIS

27th

Annual ACFE Global Fraud Conference ©2016 2

NOTES representation often provides the most effective means of

interpreting the results of the calculations, as well as

“teaching tools” when providing testimony in cases.

Detailed evidence is essential for prosecution, and

multiple methods are necessary in all investigations.

Choose analytical procedures that meet the needs of the

engagement and apply results to benchmarks. Begin with

preliminary ratios such as liquidity, debt and profitability

ratios, and then move to more advanced analytical

techniques. Ratios measure the ending balances in the

financial statement to the prior year’s ending balances but

do not measure the changes in those balances, while

indices do. Although limited in use when compared to

using indices, ratios are part of the foundation for more

advanced forensic analyses of financial information.

Cash flow statements are probably the most

misunderstood in a set of financial statements, but they

provide a wealth of information for the reviewer. Cash

flow statements provide the foundation for understanding

the relationships between the various account balances on

the balance sheet. For example, when a receivable is paid,

then cash increases. Cash flow statements cannot be

altered easily to hide fraudulent transactions within a set

of financial statements. If cash flow statements are

missing from a set of financial statements, it is easier to

falsify the amounts on the balance sheet and income

statement. In the case of a small manufacturing company,

cash flow statements were not part of the financial

statements provided monthly to the shareholders.

However, it does not take much effort to build a cash flow

statement to determine whether the financial statements

are accurate. The cash flow statements in the presentation

of this company were prepared using the information from

the company’s tax returns.

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CASE STUDIES IN ADVANCED FINANCIAL STATEMENT ANALYSIS

27th

Annual ACFE Global Fraud Conference ©2016 3

NOTES Important note to remember with cash flow statements is

that they should never be “plugged” to balance other than

for rounding issues. If a cash flow statement does not flow

correctly, there are noncash transactions in the accounting

records that have not been recognized and properly stated

as such.

The cash flow and net income ratio is very simple to use

and quite effective when graphing cash from operations to

net income. The formula, net income from operations

minus cash flow from operations divided by net income

from operations, should have its numerator at

approximately zero or a negative number. Remember that

depreciation and amortization expenses are subtracted

from income and not cash flows, so the net income from

operations number should be less than the cash flow from

operations number.

When using a dual axis chart, such as the one on the

following page representing net income and cash realized

from operations (CRO), it becomes very easy to find the

unusual pattern in the information provided in the

financial statements. CRO and net income should follow

each other rather than going in opposite directions.

-0.50

0.00

0.50

1.00

1.50

2.00

$0

$500,000

$1,000,000

$1,500,000

$2,000,000

$2,500,000

YR 2 YR 3 YR 4 YR 5

Net Income CRO

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CASE STUDIES IN ADVANCED FINANCIAL STATEMENT ANALYSIS

27th

Annual ACFE Global Fraud Conference ©2016 4

NOTES The CRO and net income comparison in the chart was

prepared from the financial statements of a wholesale

supply company. While CRO should flow the same as net

income, from YR 4 to YR 5, net income has not changed,

while CRO is increasing rapidly. This is definitely not “the

norm” and should be a warning sign that further

investigation is required.

Another useful ratio, the operating performance ratio, is

often a very good first step for analyzing net income and

sales. The formula is quite simple—net income divided by

net sales. Larger values are desirable, and adding fictitious

revenues dramatically increases the ratio. If this ratio

increases significantly, then other analytical tests and

statistical tests measure and confirm the excessive change.

While horizontal analysis is a “standard practice” in

comparing financial statements from year to year, the

vertical analysis provides a better picture of the operations

of a company. Often called common-sizing, all of the other

accounts in the income statement are measured on a

percentage of sales revenue. This method investigates the

relationships between the accounts and allows the

reviewer to focus on percentage changes from year to year

compared to numerical differences. The method also

allows the reviewer to focus on changes in operations

from year to year and removes external factors such as

competition decreasing sales. The percentages should

remain reasonably constant from period to period unless

there are operational changes internally.

While the techniques listed above are more common

analytical tools for financial information, there are two

tools that provide a new way to look at financial

statements and take a relatively short time to prepare: Lev-

Thiagarajan’s 12 Signals and Piotroski’s F-Score Model.

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CASE STUDIES IN ADVANCED FINANCIAL STATEMENT ANALYSIS

27th

Annual ACFE Global Fraud Conference ©2016 5

NOTES Both models were used previously to measure different

components of financial information. Lev-Thiagarajan’s

12 Signals was used to measure values of corporate

securities, while Piotroski’s F-Score Model was used to

measure a stock’s financial strength. Both models provide

a simple system of analyzing financial statements using a

point method based on simple calculations. While

Piotroski’s F-Score Model relies only on a “1 or 0” point

system, the Lev-Thiagarajan’s 12 Signals allows for a “not

applicable” selection as well. It is best to ignore the

signals that are not applicable to the financial statements.

The focal point for Lev-Thiagarajan’s Twelve Signals is

the direction in which the positives and negatives flow

from year to year, as shown in the chart in the presentation

representing the calculations of a manufacturing company.

In the case of this manufacturing company, the negatives

tend to increase from YR 2 through YR 4. Then in YR 5,

the company reverses the trend and had more positives

than negatives. This change becomes the focus of further

investigative analysis. In the first three years of analysis,

the accountant was embezzling funds and hiding the

missing cash throughout various accounts in the financial

statements.

While Piotroski’s F-Model uses nine variables compared

to the 12 Signals of Lev-Thiagarajan’s 12 Signals, the

model offers more in terms of analyzing not only the

financial statements but also the operating efficiency of a

company. Secondly, this model does not require any

market values, so it is useful for private company financial

statements. There are four components of the model that

measure profitability, three components that measure

liquidity, and three components that measure the operating

efficiency.

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CASE STUDIES IN ADVANCED FINANCIAL STATEMENT ANALYSIS

27th

Annual ACFE Global Fraud Conference ©2016 6

NOTES Profitability

Current year income variable

Current year operating cash flow variable

Comparison of current year income to current year

operating cash flow variable

Comparison of prior year net income to prior year

operating cash flow variable

Liquidity

Current year ratio of LTD to total assets compared

to prior year ratio

Comparison of current year current ratio to prior

year current ratio

Current year outstanding shares compared to prior

year outstanding shares

Operating efficiency

Comparison of current year gross margin to prior

year growth margin

Comparison of the percentage increase in sales to

the percentage increase in total assets

The best method to analyze Piotroski’s F-Score model is

to use a graphical representation of the points by the three

components as noted in the chart below from the analysis

of a primary government.

0

1

2

3

4

YR 2YR 3

YR 4YR 5

3 3

4 4

0 0

1 1

0

1 1

1

Profitability Liquidity Operating Efficiency

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CASE STUDIES IN ADVANCED FINANCIAL STATEMENT ANALYSIS

27th

Annual ACFE Global Fraud Conference ©2016 7

NOTES The chart poses an interesting question with just a simple

analysis: How does a company show profitability in Year

2 without liquidity and very low levels of operating

efficiency? If a company is experiencing growth in its

infrastructure and increases in fixed assets are confirmed

in the financial statement comparison over the prior year,

the results of that initiative would be similar to the results

noted in Year 2 of the chart. Otherwise, the question

becomes an issue of possible anomalies in the financial

statements. In the case of this municipality, large but well-

hidden embezzlement activity had been occurring over the

course of several years. Fixed assets that did not exist

were posted to the government-wide financial statements,

and depreciation expense was based on these “imagined”

assets.

Beneish M-Score

The Beneish M-Score model provides a wealth of

information about an organization’s financial statements

by using indices to measure changes from period to

period, whether the periods are monthly, quarterly, or

annually. While both the Lev-Thiagarajan 12 Signals and

Piotroski’s F- Score Model use a simple point system, this

model uses a weighted average methodology in its

calculations. When using the formula’s overall

calculations, if the M-Score is greater than a -2.22, the

score suggests a higher probability of financial statement

manipulation. The one item to remember, though, is the

movement of negative numbers in order to accurately

determine the possibility of financial statement

manipulation. For example, a -2.21 is actually greater than

a -2.22 while a -2.23 is actually less than the -2.22.

– Less Zero More +

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CASE STUDIES IN ADVANCED FINANCIAL STATEMENT ANALYSIS

27th

Annual ACFE Global Fraud Conference ©2016 8

NOTES In addition to the overall M-Score formula, each

component of the formula provides its own analytic to

assess the possibility of unusual variations in the financial

statements. Each component also has its own benchmark

for comparison. The components consist of the following:

Days Sales in Receivable Index (DSRI)

Gross Margin Index (GMI)

Asset Quality Index (AQI)

Sales Growth Index (SGI)

Depreciation Index (DI or DEPI)

Sales and General Administrative Expenses (SGAI or

SGAEI)

Leverage Index (LI or LVGI)

Total accruals to total assets (TATA)

The general benchmark for all of the components, with the

exception of TATA, is 1. The general benchmark of

TATA is zero. Calculations might vary slightly from the

benchmarks, but remember that the Beneish Model

measures changes in small increments, and multiple

periods of financial statements are necessary to measure

the changes accurately.

The movement of the component calculation related to its

benchmark provides the following information about the

financial statements:

When DSRI is greater than 1, it suggests that accounts

receivable and sales are not maintaining a stable

relationship and increases the possibility of potential

earnings manipulation.

When GMI is greater than 1, it indicates a decline in

gross profit in the current year and might suggest

exploitation of inventory or cover-up of embezzlement

activities.

When AQI is greater than 1, it indicates the possibility

of deferring costs, such as incorrectly capitalizing

costs.

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CASE STUDIES IN ADVANCED FINANCIAL STATEMENT ANALYSIS

27th

Annual ACFE Global Fraud Conference ©2016 9

NOTES

-1.16

-3.23

-2.42

0.27

YR 1 YR 2 YR 3 YR 4

When SGI is greater than 1, it indicates faster rates of

growth. While this by itself is good for a company, it

also represents higher risk elements. A company might

be growing faster than it is able to maintain its internal

control structure.

When DEPI is greater than 1, it indicates that the rate

of depreciation has slowed by changing the useful

lives of equipment or changing depreciation methods.

SGAI is usually stable, and disproportionate decreases

might suggest financial statement manipulation using

timing differences in recording transactions.

Disproportionate increases might suggest hidden costs

related to fraudulent activity.

When LVGI is greater than 1, it indicates new or

increased debt loading, creating higher risk elements

related to financial statement misrepresentation in

order to meet debt covenants.

TATA is also typically stable, and positive

calculations indicate a higher level of accruals and

therefore less cash, creating a higher risk for financial

statement misrepresentation. When this calculation is

positive, advanced accrual analysis calculations will

determine whether the accruals represent future cash

flows.

The chart below shows the calculations for the Beneish M-

Score model for a wholesale supply company.

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CASE STUDIES IN ADVANCED FINANCIAL STATEMENT ANALYSIS

27th

Annual ACFE Global Fraud Conference ©2016 10

NOTES The calculations show that both Year 1 and Year 4

indicate the possibility of financial statement manipulation

because the amounts are greater than a -2.22. This

company had issues with missing inventory.

Many times, it is very beneficial to create a chart of

several components of the Beneish M- Score Model to

compare the relationships within the financial statement

accounts. The following chart is an example of analyzing

the relationships associated with inventory from a

manufacturing company.

The chart poses several questions concerning Year 2 and

Year 3. In Year 2 there is a significant increase in SGI,

indicating accelerated sales growth, but inventory did not

increase, suggesting the use of inventoried items covering

some of the sales. Secondly, the GMI calculation indicates

that gross margin had decreased from the prior year,

suggesting the manipulation of inventory.

In Year 3, sales decreased substantially over the prior

year. There was a decrease in GMI and a decrease in TITA

compared to the prior year, with a more substantial

decrease in TITA compared to GMI. Usually, as sales

decrease, inventory increases, therefore TITA will

increase. Year 4 and Year 5 show this relationship. The

0

0.5

1

1.5

2

2.5

3

YR 2 YR 3 YR 4 YR 5

1.17

0.98 0.92

1.02

2.57

1.43

0.960.78

1.17

0.93

1.01 1.15

GMI SGI TITA

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CASE STUDIES IN ADVANCED FINANCIAL STATEMENT ANALYSIS

27th

Annual ACFE Global Fraud Conference ©2016 11

NOTES CFO of this company misappropriated cash and generated

fictitious invoices for raw materials to cover the theft of

cash. The CFO also took small inventory items from the

company and sold them below cost to the company’s

customers through a company set up by the CFO for this

purpose. Year 4 and Year 5 calculations represent normal

operations and activity since the company terminated the

CFO in the latter part of Year 3.

The Accruals

There are three different analytics used to measure

accruals recorded in the financial statements: Dechow-

Dichev Accrual Quality, Sloan’s Accruals, and Jones

Nondiscretionary Accruals. Each model performs a

specific task concerning accruals. Dechow-Dichev

Accrual Quality measures the stability and the quality of

the accruals in terms of generating future cash flows. Poor

quality indicates poor generation of future cash flows. The

Dechow-Dichev implied earnings calculation measures the

influence of accruals to net income. Sloan’s Accruals

calculates the implied cash component of earnings and is

used not only for monthly, quarterly, or annual financial

information but also for specific time periods outside of

normal financial statement preparation, such as six weeks,

nine weeks, etc. While Jones Nondiscretionary Accruals

actually measure these types of accruals, Jones determined

that by measuring nondiscretionary accruals, discretionary

accruals are measured indirectly.

The important feature to remember for these models is the

term accruals. Accruals mean all financial statement

accounts that are not cash only. For these models,

accounts receivables, accounts payables, accrued payroll

and other types of accrual accounts are measured to

determine the quality of the accruals, as well as the

implied future earnings and cash flows. Examples of

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CASE STUDIES IN ADVANCED FINANCIAL STATEMENT ANALYSIS

27th

Annual ACFE Global Fraud Conference ©2016 12

NOTES discretionary accruals are the allowances and reserve

accounts that depend upon management’s assertions and

representations. As nondiscretionary accruals decrease,

discretionary accruals increase.

The best method to analyze both the Dechow-Dichev

Accrual Quality and Earnings calculations is to compare

the calculations to net income. The important factor to

remember in analyzing these calculations is that the

Dechow-Dichev calculations should follow the same

movement as net income. If they move in opposite

directions, then there are unusual variations in the

financial statement information that must be examined.

There are certain conditions that affect the Dechow-

Dichev Accrual Quality calculation that the forensic

accountant needs to consider in evaluating the results.

These conditions include a longer operating cycle, the size

of the company, stability of sales, continued losses, and

the amount of accruals are just some of these factors.

The slides in the presentation represent these calculations

and comparisons to net income for a financial institution

in the midst of two different types of fraudulent activity—

embezzlement and financial statement fraud. The chart on

the following page clearly indicates opposite movement

between net income and the Dechow-Dichev Accrual

Quality for Year 3 and Year 4.

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CASE STUDIES IN ADVANCED FINANCIAL STATEMENT ANALYSIS

27th

Annual ACFE Global Fraud Conference ©2016 13

NOTES

The best method for analyzing Sloan’s Accruals

calculations is to compare net income, the implied cash

component, and the accrual component of the calculations.

Higher levels of the accrual component compared to net

income and the implied cash component is a key factor.

The implied cash calculation represents future anticipated

cash flow. The chart on the following page from the

presentation showing the calculations for a manufacturing

company with embezzlement issues clearly defines the

higher accrual component for Year 2 and Year 5. The

most notable observation for Year 5 is that net income and

the accrual component calculations are almost equal, while

the implied cash component is negative.

(0.03)

(0.02)

(0.01)

-

0.01

0.02

0.03

0.04

0.05

0.06

$0

$20,000

$40,000

$60,000

$80,000

$100,000

$120,000

$140,000

$160,000

$180,000

YR 2 YR 3 YR 4 YR 5Net Income Dechow-Dichev

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CASE STUDIES IN ADVANCED FINANCIAL STATEMENT ANALYSIS

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Annual ACFE Global Fraud Conference ©2016 14

NOTES

The important point to remember in analyzing Jones

Nondiscretionary Accruals is that as nondiscretionary

accruals decrease, discretionary accruals increase, thus

creating more influence in the financial statements. Since

discretionary accruals represent accounts in the financial

statements that are based primarily on management’s

assertions and representations, these accounts represent a

higher risk for potential fraudulent activity. The chart in

the presentation shows a significant decline in

nondiscretionary accruals beginning in Year 3 through

Year 5. In Year 5, the calculations indicate that the

discretionary accruals are the only accruals affecting the

financial statements. These calculations are from a

manufacturing company that has the following

discretionary accounts: allowance for bad debts, warranty

reserves, and inventory reserves. Both the warranty

reserves and the inventory reserves were understated in

Year 3 through Year 5 in an attempt to show more profit

to its investors compared to the actual losses the company

was incurring.

(1,500,000)

(1,000,000)

(500,000)

-

500,000

1,000,000

1,500,000

2,000,000

2,500,000

YR 2 YR 3 YR 4 YR 5

Net Income Accrual Component Implied Cash Component

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CASE STUDIES IN ADVANCED FINANCIAL STATEMENT ANALYSIS

27th

Annual ACFE Global Fraud Conference ©2016 15

NOTES Benford’s Law is an analytical technique that measures the

reasonableness of financial information. It is well known

as a great tool for large data sets; it can be used on smaller

data sets as long as the data set meets the following

requirements:

The mean is greater than the median.

The skew is positive.

Most do not consider this technique in analyzing financial

statements, but it is useful as a “reasonableness” test when

using a 95 percent confidence level indicating that there is

a 5 percent chance of error in the conclusions reached

from the testing. By using the first two digits test instead

of the first digit test, there is a smaller chance for “false-

positives” requiring additional analysis that is not

necessary. When combined with the 95 percent confidence

level, the chance for false-positives decreases even more.

The most important aspect of Benford’s Law as it relates

to embezzlement activity is that an amount on a fictitious

invoice will not follow the rules of Benford’s Law no

matter how clever the embezzler thinks he or she is.

Z-Scores measure variability in the data and easily point to

anomalies in financial information. The formula is found

in Excel and is easily accessible. There are two statistical

rules that apply to the analysis of Z-Scores— the

Empirical Rule and the Chebyshev’s Theorem. The

Empirical Rule is for data sets that exhibit the

characteristics of normally distributed sets of data. The

easiest way to determine this is to chart the data and

compare it to the picture below. Generally, the graph looks

like a top hat.

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CASE STUDIES IN ADVANCED FINANCIAL STATEMENT ANALYSIS

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Annual ACFE Global Fraud Conference ©2016 16

NOTES

The presentation includes the rules as they apply to

distance from the mean measured by the standard

deviation. While this seems complicated, it is very easy to

remember the following as it applies to financial

information:

If the Z-Score of a number is less than -2 or more than

2, this number is occurring about 5 percent of the time

in the data, so it is unusual and possibly and outlier.

If the Z-Score of a number is less than -3 or more than

3, this number is occurring about 1 percent of the time

in the data, so it is very unusual and probably an

outlier.

For other sets of data that do not meet the characteristics

of normally distributed sets of data, the Chebyshev’s

Theorem applies. When graphed, these data sets will

definitely not have the “top-hat” look but will exhibit

other shapes and sizes as noted in the picture below.

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NOTES They might even have two humps within the data or be

“skewed” either to the right, as in the picture, or to the left.

No matter what the shape, Chebyshev’s Theorem is the

tool to use.

Chebyshev’s Theorem does measure the Z-score of the

number the same way as the Empirical Rule with the

following exceptions:

If the Z-Score of a number is less than -3 and more

than 3, the number occurs about 11 percent of the time

in the data set, and it is unusual and possibly an

outlier.

If the Z-Score is less than -4 or more than 4, the

number occurs less than 6 percent of the time in the

data set, and it is very unusual and probably an outlier.

All of these techniques determine areas of financial

information that require additional study to determine the

reasons for the anomalies, whether the information is truly

unusual for the company’s operations or they represent

fraudulent activity. They are applicable for all types of

companies and industries just by defining some of the

terms in the models to the applicable type of company

under review. For example, SGI for a nonprofit

organization might measure the different types of program

revenues. Do not let the terms used in the formulas of

these techniques restrict their use—be creative!

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27th Annual ACFE Global Fraud Conference ©2016 18

NOTES Sources used in the presentation and in the previous pages

include the following:

Beneish, Messod D. 1999. “The Detection of Earnings

Manipulation.” Financial Analysts Journal 55 (5): 24–36.

Dechow, Patricia M. and Ilia D. Dechev. 2002. “The

Quality of Accruals and Earnings: The Role of Accrual

Estimation Errors.” The Accounting Review 77: 35–59.

Jones, Jennifer J. 1991. “Earnings Management During

Import Relief Investigations.” Journal of Accounting

Research 29 (2).

Lev, Baruch and Ramu Thiagarajan. 1993. “Fundamental

Information Analysis.” Journal of Accounting Research 31

(2).

Mantone, Pamela. 2013. Using Analytics to Detect

Possible Fraud: Tools and Techniques. New Jersey: John

Wiley & Sons, Inc.

Nigrini, Mark J. 1999. “I’ve Got Your Number.” Journal

of Accountancy 87 (5): 79.

Nigrini, Mark J. and Linda J. Mittermaier. 1997. “The Use

of Benford’s Law as an Aid in Analytical Procedures.”

Auditing: A Journal of Practice & Theory 16 (2): 52.

Pelosi, Marilyn K. and Theresa M. Sandifer. 2002. Doing

Statistics for Business with Excel. New Jersey: John Wiley

& Sons, Inc.

Piotroski, Joseph D. 2002. “Value Investing: The Use of

Historical Financial Statement Information to Separate

Winners from Losers.” Journal of Accounting Research

38.

Sloan, Richard D. 1996. “Do Stock Prices Fully Reflect

Information in Accruals and Cash Flows About Future

Earnings?” The Accounting Review 71 (3): 289–315.