world corrupt report 2011
TRANSCRIPT
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REPORT TO THE NATIONSO N O C C U P A T I O N A L F R A U D A N D A B U S E
2010 Global Fraud Study
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Lr rm prdn
When the ACFE published its rstReport to the Nation on Occupational Fraud and Abuse in 1996,
it broke new ground in anti-raud research by providing an analysis o the costs, the methodologies
and the perpetrators o raud within U.S. organizations. The collective body o knowledge con-
tained in the rst ve editions o the Report to the Nation published between 1996 and 2008
has become the most authoritative and widely quoted research publication on occupational
raud.
Now, or the rst time, the data contained in the Report have been drawn rom raud cases
throughout the world. As readers will see, it refects the truly universal nature o occupational raud. This expansion o
our research is denoted in the modied title or this study, which has now become the Report to the Nations on Occu-
pational Fraud and Abuse.
The inormation contained in this report is based on 1,843 cases o occupational raud that were reported by the Certied
Fraud Examiners (CFEs) who investigated them. These oenses occurred in more than 100 countries on six continents,
and more than 43% took place outside the United States. What is perhaps most striking about the data we gathered is
how consistent the patterns o raud are around the globe. While some regional dierences exist, or the most part oc-
cupational raud seems to operate similarly whether it occurs in Europe, Asia, South America or the United States.
The Report to the Nations is the brainchild o the ACFEs ounder and Chairman, Dr. Joseph T. Wells, CFE, CPA who
throughout his career has contributed more to the study o raud and the development o the anti-raud proession than
any other person. On behal o the ACFE, and in honor o its ounder, Dr. Wells, I am pleased to present the2010 Report to
the Nations on Occupational Fraud and Abuse to practitioners, business and government organizations, academics, the
media and the general public throughout the world. The inormation contained in this Report will be invaluable to those
who seek to deter, detect, prevent or simply understand the global economic impact o occupational raud.
James D. Ratley, CFE
President,
Association o Certied Fraud Examiners
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Table of Contents
exv smmry .............................................................................................................................................................4
ir ..........................................................................................................................................................................6
th c f o Fr ..........................................................................................................................................8
DistributionofLosses
Hw o Fr i cmm ............................................................................................................................10
AssetMisappropriationSub-Schemes
DurationofFraudSchemes
d f Fr shm .............................................................................................................................................16
InitialDetectionofOccupationalFraudSchemes
SourceofTips
ImpactofHotlines
DetectionMethodsBasedonOrganizationType
DetectingFraudinSmallBusinesses
DetectionofOccupationalFraudBasedonRegion
Vm orgz ..........................................................................................................................................................24
GeographicalLocationofOrganizations
TypeofOrganizations
SizeofOrganizations
MethodsofFraudinSmallBusinesses
IndustryofOrganizations
Anti-FraudControlsatVictimOrganizations Anti-FraudControlsatSmallBusinesses
Anti-FraudControlsbyRegion
EffectivenessofControls
ImportanceofControlsinDetectingorLimitingFraud
ControlWeaknessesthatContributedtoFraud
ModicationofControls
prrr ........................................................................................................................................................................48
PerpetratorsPosition
PerpetratorsGender
PerpetratorsAge
PerpetratorsTenure
PerpetratorsEducationLevel PerpetratorsDepartment
PerpetratorsCriminalandEmploymentHistory
BehavioralRedFlagsDisplayedbyPerpetrators
Mhgy ......................................................................................................................................................................75
ax brkw f Ggrh Rg y cry ..........................................................................................78
Fr prv chk ................................................................................................................................................80
a h acFe ..................................................................................................................................................................82
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summary Fndng
Survey participants estimated that the typicalorganization loses 5% o its annual revenue toraud. Applied to the estimated 2009 Gross WorldProduct, this gure translates to a potential totalraud loss o more than $2.9 trillion.
The median loss caused by the occupationalraud cases in our study was $160,000. Nearlyone-quarter o the rauds involved losses o atleast $1 million.
The rauds lasted a median o 18 months beorebeing detected.
Asset misappropriation schemes were the mostcommon orm o raud in our study by a widemargin, representing 90% o cases though theywere also the least costly, causing a median losso $135,000. Financial statement raud schemeswere on the opposite end o the spectrum in bothregards: These cases made up less than 5% othe rauds in our study, but caused a median losso more than $4 million by ar the most costlycategory. Corruption schemes ell in the middle,
comprising just under one-third o cases andcausing a median loss o $250,000.
Occupational rauds are much more likely to bedetected by tip than by any other means. Thisnding has been consistent since 2002 when webegan tracking data on raud detection methods.
Small organizations are disproportionatelyvictimized by occupational raud. Theseorganizations are typically lacking in anti-raudcontrols compared to their larger counterparts,which makes them particularly vulnerable to raud.
The industries most commonly victimized in our
study were the banking/nancial services,manuacturing and government/publicadministration sectors.
Anti-raud controls appear to help reduce the costand duration o occupational raud schemes. Welooked at the eect o 15 common controls onthe median loss and duration o the rauds. Victimorganizations that had these controls in place hadsignicantly lower losses and time-to-detection thanorganizations without the controls.
High-level perpetrators cause the greatestdamage to their organizations. Frauds commit-ted by owners/executives were more than threetimes as costly as rauds committed by managers,and more than nine times as costly as employeerauds. Executive-level rauds also took muchlonger to detect.
excuv summary
This Report is based on data
compiled from a study of 1,843
cases of occupational fraud that
occurred worldwide between
January 2008 and December
2009. All information was pro-
vided by the Certied Fraud Ex-aminers (CFEs) who investigated
those cases. The fraud cases in
our study came from 106 nations
with more than 40% of cases
occurring in countries outside
the United States providing a
truly global view into the plague
of occupational fraud.
One-fourth of the frauds in this Reportcaused at least $1 million in losses.
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More than 80% o the rauds in our studywere committed by individuals in one o sixdepartments: accounting, operations, sales,executive/upper management, customer serviceor purchasing.
More than 85% o raudsters in our study hadnever been previously charged or convicted ora raud-related oense. This nding is consistentwith our prior studies.
Fraud perpetrators oten display warning signs
that they are engaging in illicit activity. The mostcommon behavioral red fags displayed by theperpetrators in our study were living beyond theirmeans (43% o cases) and experiencing nancialdiculties (36% o cases).
Cnclun and Rcmmndan
Occupational raud is a global problem. Thoughsome o our ndings dier slightly rom region toregion, most o the trends in raud schemes, per-petrator characteristics and anti-raud controls aresimilar regardless o where the raud occurred.
Fraud reporting mechanisms are a criticalcomponent o an eective raud prevention anddetection system. Organizations should implementhotlines to receive tips rom both internal andexternal sources. Such reporting mechanismsshould allow anonymity and condentiality, andemployees should be encouraged to reportsuspicious activity without ear o reprisal.
Organizations tend to over-rely on audits. Externalaudits were the control mechanism most widelyused by the victims in our survey, but they rankedcomparatively poorly in both detecting raud andlimiting losses due to raud. Audits are clearly
important and can have a strong preventativeeect on raudulent behavior, but they should notbe relied upon exclusively or raud detection.
Employee education is the oundation opreventing and detecting occupational raud.Sta members are an organizations top rauddetection method; employees must be trained inwhat constitutes raud, how it hurts everyone inthe company and how to report any questionableactivity. Our data show not only that most raudsare detected by tips, but also that organizationsthat have anti-raud training or employees andmanagers experience lower raud losses.
Surprise audits are an eective, yet underutilized,tool in the ght against raud. Less than 30% ovictim organizations in our study conductedsurprise audits; however, those organizationstended to have lower raud losses and to detectrauds more quickly. While surprise audits can beuseul in detecting raud, their most importantbenet is in preventing raud by creating a percep-tion o detection. Generally speaking, occupationalraud perpetrators only commit raud i theybelieve they will not be caught. The threat osurprise audits increases employees perception
that raud will be detected and thus has a strongdeterrent eect on potential raudsters.
Small businesses are particularly vulnerable toraud. In general, these organizations have ar ewercontrols in place to protect their resources romraud and abuse. Managers and owners o smallbusinesses should ocus their control investmentson the most cost-eective mechanisms, suchas hotlines and setting an ethical tone or theiremployees, as well as those most likely to helpprevent and detect the specic raud schemes thatpose the greatest risks to their businesses.
Internal controls alone are insucient to ully
prevent occupational raud. Though it is importantor organizations to have strategic and eectiveanti-raud controls in place, internal controls willnot prevent all raud rom occurring, nor will theydetect most raud once it begins.
Fraudsters exhibit behavioral warning signs o theirmisdeeds. These red fags such as living beyondones means or exhibiting control issues will notbe identied by traditional controls. Auditors andemployees alike should be trained to recognize thecommon behavioral signs that a raud is occurringand encouraged not to ignore such red fags, as
they might be the key to detecting or deterring araud.
Given the high costs o occupational raud,eective raud prevention measures are critical.Organizations should implement a raud preventionchecklist similar to that on page 80 in order to helpeliminate raud beore it occurs.
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A wide variety o crimes and swindles all under the um-
brella o raud. From Ponzi schemes and identity thet to
data breaches and alsied expense reports, the ways
perpetrators attempt to part victims rom their money are
extremely diverse and continually evolving. At their core,
however, all rauds involve a violation o trust.
For businesses, no trust violations have the potential to be
as harmul as those committed by the very individuals who
are relied upon to make the organization successul: its
employees. This report ocuses on the category o raud
occupational raud in which an employee abuses his
or her position within the organization or personal gain.
More ormally, occupational raud may be dened as:
The use of ones occupation for personal enrichment
through the deliberate misuse or misapplication of
the employing organizations resources or assets.
This denition is very broad, encompassing a wide rangeo misconduct by employees at every organizational level.
Occupational raud schemes can be as simple as pilerage
o company supplies or manipulation o timesheets, or as
complex as sophisticated nancial statement rauds.
One o the ACFEs primary missions is to educate anti-
raud proessionals and the general public about the seri-
ous threat occupational raud poses. To that end, we have
undertaken extensive research to provide an in-depth look
at the costs and trends in occupational raud. In 1996, theACFE released its Report to the Nation on Occupational
Fraud and Abuse, which was the largest known privately
unded study on the subject at the time.
The stated goals o the rst Report were to:
Summarize the opinions o experts on the percentage and amount o organizational revenue lost to allorms o occupational raud and abuse.
Examine the characteristics o the employees whocommit occupational raud and abuse.
Determine what kinds o organizations are victimso occupational raud and abuse.
Categorize the ways in which serious raud andabuse occur.
Since the inception o the Report to the Nation more than
a decade ago, we have released ve updated editions in
2002, 2004, 2006, 2008 and the current version in 2010. Like
the rst Report, each subsequent edition has been based
on detailed case inormation provided by Certied Fraud Ex-
aminers (CFEs). With each new edition o the Report, we
add to and modiy the questions we ask o our survey par-
ticipants in order to enhance the quality o the data we col-
lect. This evolution o theReport to the Nation has enabled
us to continue to draw more meaningul inormation rom
the experiences o CFEs and the rauds they encounter.
In our 2010 Report, we have, or the rst time ever, wid-
ened our study to include cases rom countries outside
the United States. This expansion allows us to more ully
explore the truly global nature o occupational raud and
provides an enhanced view into the severity and impact
o these crimes. Additionally, we are able to compare the
anti-raud measures taken by organizations worldwide inorder to give raud ghters everywhere the most appli-
cable and useul inormation to help them in their raud
prevention and detection eorts.
inrducn
A N Radr: Throughout this Report, we have included several comparisons o our current ndings with those rom our 2008 Report. However, it is important to note thatthe 2010 data include reported rauds rom CFEs in 106 countries, while the 2008 data pertain to rauds reported only by CFEs in the United States. Although the populations orespondents or the two studies are not entirely analogous, we have nonetheless included these prior-study comparisons, as we believe interesting and useul trends can be seenby comparing and contrasting the rauds reported in the two studies. To enhance data clarity, we have included comparisons o 2008 data with both all-case data and U.S.-only datarom our 2010 research when noteworthy discrepancies in our current ndings are present.
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Opationa Fra an Ase cassifation System
Corruption
Conflicts ofInterest
Cash
FraudulentDisbursements
PurchasingSchemes
SalesSchemes
Bid Rigging
OtherOther
Larceny Skimming Misuse Larceny
AssetRequisitionsand Transfers
False Salesand Shipping
Purchasingand Receiving
UnconcealedLarceny
Sales
Unrecorded Write-offSchemes
LappingSchemes
Unconcealed
Understated
ReceivablesRefunds
and Other
Other
Cash onHand
From theDeposit
Billing Schemes Payroll SchemesExpense
ReimbursementSchemes
Check TamperingRegister
Disbursements
Forged Maker False Voids
False RefundsForged
Endorsement
ConcealedChecks
AuthorizedMaker
Altered Payee
MischaracterizedExpenses
GhostEmployees
CommissionSchemes
OverstatedExpenses
FictitiousExpenses
MultipleReimbursements
WorkersCompensation
Falsified Wages
Shell Company
Non-AccompliceVendor
PersonalPurchases
InvoiceKickbacks
Asset/RevenueOverstatements
TimingDifferences
FicticiousRevenues
ImproperDisclosures
ConcealedLiabilities and
Expenses
ImproperAsset
Valuations
Asset/RevenueUnderstatements
EmploymentCredentials
InternalDocuments
ExternalDocuments
IllegalGratuities
EconomicExtortion
Non-Cash
Non-Financial
Bribery Financial
AssetMisappropriation
FraudulentStatements
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Measuring the cost o occupational raud is an important,
yet incredibly challenging, endeavor. Arguably, the true
cost is incalculable. The inherently clandestine nature
o raud means that many cases will never be revealed,
and, o those that are, the ull amount o losses might not
be uncovered, quantied or reported. Consequently, any
measurement o occupational raud costs will be, at best,
an estimate. Nonetheless, determining such an approxi-
mation is critical to illustrate the pandemic and destruc-
tive nature o white-collar crime.
We asked each CFE who participated in our survey to pro-
vide his or her best estimate o the percentage o annual
revenues that the typical organization loses to raud in a
given year. The median response was that the average
organization annually loses 5% o its revenues to raud.
Applying this percentage to the 2009 estimated Gross
World Product o $58.07 trillion1 would result in a pro-
jected total global raud loss o more than $2.9 trillion.
Readers should note that this estimate is based solely
on the opinions o 1,843 anti-raud experts, rather than
any specic data or actual observations; accordingly, it
should not be interpreted as a literal representation o the
worldwide cost o occupational raud. However, because
there is no way to precisely calculate the size o global
raud losses, the best estimate o anti-raud proession-
als with a rontline view o the problem may be as reli-
able a measure as we are able to make. In any event, it
is undeniable that the overall cost o occupational raud
is immense, certainly costing organizations hundreds o
billions or trillions o dollars each year.
t C occuanal Fraud
1United States Central Intelligence Agency, The World Factbook (https://www.cia.gov/library/publications/the-world-actbook/geos/xx.html)
Fraud, by its very nature, does
not lend itself to being scien-
tically observed or measured
in an accurate manner. One of
the primary characteristics of
fraud is that it is clandestine,
or hidden; almost all fraud in-volves the attempted conceal-
ment of the crime.
The typical organization loses 5% of itsannual revenues to occupational fraud.
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Drbun L
We received inormation about the total dollar loss or 1,822 o the 1,843 rauds reported to us in our study.2 The median
loss or these cases was $160,000. Nearly one-third o the raud schemes caused a loss to the victim organization o
more than $500,000, and almost one-quarter o all reported cases topped the $1 million threshold.
distrition o doar losses
2Although this Report includes raud cases rom more than 100 nations, all monetary amounts presented throughout this Report are in U.S. dollars.
0%
5%
10%
15%
20%
25%
30%
$1,000,000and up
$500,000 $999,999
$100,000 $499,999
$50,000 $99,999
$10,000 $49,999
$1,000 $9,999
Less than$1,000
Dollar Loss
PercentofCases
2.4%
7.2%
18.4%
10.6%
29.3%
8.4%
23.7%
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Previous ACFE research has identied three primary cat-
egories o occupational raud used by individuals to de-
raud their employers.Asset misappropriations are those
schemes in which the perpetrator steals or misuses an
organizations resources. These rauds include schemes
such as skimming cash receipts, alsiying expense re-
ports and orging company checks.
Corruption schemes involve the employees use o his orher infuence in business transactions in a way that vio-
lates his or her duty to the employer or the purpose o
obtaining a benet or him- or hersel or someone else.
Examples o corruption schemes include bribery, extor-
tion and a confict o interest.
Financial statement fraud schemes are those involving
the intentional misstatement or omission o material in-
ormation in the organizations nancial reports. Common
methods o raudulent nancial statement manipulation
include recording ctitious revenues, concealing liabili-
ties or expenses and articially infating reported assets.
As indicated in the ollowing charts, asset misappropriations
are by ar both the most requent and the least costly orm
o occupational raud. On the other end o the spectrum are
cases involving nancial statement raud. These schemes
were present in less than 5% o the cases reported to us,
but caused a median loss o more than $4 million. Corrup-
tion schemes ell in the middle category in both respects,
occurring in just under one-third o all cases involved in our
study and causing a median loss o $250,000.
hw occuanal Fraud i Cmmd
Based on previous ACFE
research we have broken down
the schemes reported to us
into three primary categories:
asset misappropriation,
corruption, and nancial
statement fraud.
Financial statement fraud is the mostcostly form of occupational fraud, causinga median loss of more than $4 million.
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Opationa Fras y category Freqeny3
Opationa Fras y category Meian loss
3The sum o percentages in this chart exceeds 100% because several cases involved schemes rom more than one category.
0% 20% 40% 60% 80% 100%
2010
2008
Financial
Statement Fraud
Corruption
Asset
Misappropriation88.7%
32.8%
10.3%
86.3%
26.9%
4.8%
Percent of Cases
Typ
e
ofFraud
$0 $1,000,000 $2,000,000 $3,000,000 $4,000,000 $5,000,000
2010
2008
AssetMisappropriation
Corruption
FinancialStatement Fraud
Median Loss
Type
ofFraud
$4,100,000
$2,000,000
$250,000
$135,000
$150,000
$375,000
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As previously mentioned, our 2010 data include raud cases rom countries throughout the world, while our 2008 data
contain only U.S.-based cases. In the ollowing charts, we isolated the U.S. cases rom our current study to make a more
direct comparison to our 2008 data. Interestingly, while nancial statement raud remained the least common and most
costly orm o raud among U.S. cases, there was a much lower percentage o nancial statement cases in this study
(our percent) as compared to 2008 (ten percent). Additionally, the median losses or all three categories o raud were
notably smaller in 2010 than they were in 2008.
hw occuanal Fraud i Cmmd
Opationa Fras y category (u.S. ony) Freqeny4
4The sum o percentages in this chart exceeds 100% because several cases involved schemes rom more than one category.
0% 20% 40% 60% 80% 100%
2010
2008
Financial
Statement Fraud
Corruption
Asset
Misappropriation88.7%
21.9%
10.3%
89.8%
26.9%
4.3%
Percent of Cases
Type
ofFraud
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In addition to observing the requency and median losses
caused by the three categories o raud, we analyzed the
proportion o the total losses suered based on scheme
category. The cases in our study represented a combined
total loss o more than $18 billion. As indicated in the chart
to the right, o the total reported losses that were attribut-
able to a specic scheme type, 21% were caused by asset
misappropriation schemes, 11% by corruption and 68%
by raudulent nancial statements.
Opationa Fras y category (u.S. ony) Meian loss
Perent o Tota Reporte doar losses
$0 $500,000 $1,000,000 $1,500,000 $2,000,000
2010
2008
AssetMisappropriation
Corruption
FinancialStatement Fraud
Median Loss
Typ
e
ofFraud
$1,730,000
$2,000,000
$175,000
$100,000
$150,000
$375,000
Financial Statement Fraud
68.0%
Asset Misappropriation
20.8%
Corruption
11.3%
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hw occuanal Fraud i Cmmd
A Marran sub-scm
With nearly 90% o occupational rauds involving some orm o asset misappropriation, it is instructional to urther de-
lineate the methods used by employees to embezzle organizational assets. We divided asset misappropriation schemes
into nine sub-categories, as illustrated in the table on page 15. The rst eight sub-categories represent schemes target-
ing cash; these rauds account or approximately 85% o all asset misappropriations.
Two o the sub-schemes skimming and cash larceny involve pilering incoming cash receipts, such as sales revenues
and accounts receivable collections. The next ve sub-categories billing, expense reimbursement, check tampering,
payroll and raudulent register disbursement schemes involve raudulent disbursements o cash. The eighth orm o
cash misappropriation targets cash the organization has on hand, such as petty cash unds or cash in a vault. The nal
sub-category o asset misappropriations covers the thet or misuse o non-cash assets, including inventory, supplies, xed
assets, investments, intellectual property and proprietary inormation. The table on page 15 provides the requency and
median loss associated with each asset misappropriation sub-category.
Duran Fraud scm
In addition to examining the monetary cost o the raud cases reported to us, we analyzed the length o time these schemes
lasted beore being detected. The median duration the time period rom when the raud rst occurred to when it was
discovered or all cases in our study was 18 months. Not surprisingly, cases involving nancial statement raud the
most costly orm o raud lasted the longest, with a median duration o 27 months. Fraudulent register disbursements,
on the other hand, were not only the least costly orm o raud in our study, but also tended to be detected the soonest.
Meian dration o Fra base on Sheme Type
0 5 10 15 20 25 30
Register Disbursement
Non-Cash
Larceny
Skimming
Cash on Hand
Corruption
Billing
Payroll
Expense Reimbursements
Check Tampering
Financial Statement Fraud 27
24
24
24
24
18
18
18
18
15
12
Median Months to Detection
SchemeType
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Asset Misappropriation S-categories
Cagry Dcrn examlCa
Rrd
prcn
all ca5Mdan
L
scm invlvng t Ca Rc
Skimming Any scheme in which cash is stolen roman organizationbefore it is recorded on theorganizations books and records
Employee accepts payment rom acustomer, but does not record the sale,and instead pockets the money
267 14.5% $60,000
Cash Larceny Any scheme in which cash is stolen roman organizationafterit has been recordedon the organizations books and records
Employee steals cash and checks romdaily receipts beore they can bedeposited in the bank
181 9.8% $100,000
scm invlvng Frauduln Dburmn Ca
Billing Any scheme in which a person causeshis employer to issue a payment bysubmitting invoices or ctitious goods orservices, infated invoices or invoices orpersonal purchases
Employee creates a shell company andbills employer or services not actuallyrendered
Employee purchases personal itemsand submits invoice to employer or
payment
479 26.0% $128,000
ExpenseReimbursements
Any scheme in which an employee makesa claim or reimbursement o ctitious orinfated business expenses
Employee les raudulent expensereport, claiming personal travel,nonexistent meals, etc.
278 15.1% $33,000
Check Tampering Any scheme in which a person steals hisemployers unds by intercepting, orgingor altering a check drawn on one o theorganizations bank accounts
Employee steals blank companychecks, makes them out to himsel oran accomplice
Employee steals outgoing check to avendor, deposits it into his own bankaccount
274 13.4% $131,000
Payroll Any scheme in which an employee causeshis employer to issue a payment bymaking alse claims or compensation
Employee claims overtime or hours notworked
Employee adds ghost employees to thepayroll
157 8.5% $72,000
Cash Register
Disbursements
Any scheme in which an employee makes
alse entries on a cash register to concealthe raudulent removal o cash
Employee raudulently voids a sale on
his cash register and steals the cash
55 3.0% $23,000
or A Marran scm
Cash on HandMisappropriations
Any scheme in which the perpetratormisappropriates cash kept on hand at thevictim organizations premises
Employee steals cash rom a companyvault
121 12.6% $23,000
Non-CashMisappropriations
Any scheme in which an employee stealsor misuses non-cash assets o the victimorganization
Employee steals inventory rom awarehouse or storeroom
Employee steals or misuses condentialcustomer nancial inormation
156 16.3% $90,000
5The sum o percentages in this table exceeds 100% because several cases involved asset misappropriation schemes rom more than one category.
N: Because asset misappropriation schemes are both so common and so diverse in their methods, or the remainder
o the Report, we will break down our analysis o the raud schemes into 11 categories corruption, nancial statement
raud and the nine sub-categories o asset misappropriation so as to provide a meaningul understanding o the ull
spectrum o ways in which employees deraud their employing organizations.
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One o the principal goals o our research is to identiy
how past rauds were detected so that organizations can
apply that knowledge to their uture anti-raud eorts.
Tips were by ar the most common detection method in
our study, catching nearly three times as many rauds as
any other orm o detection. This is consistent with the
ndings in our prior reports. Tips have been ar and away
the most common means o detection in every study
since 2002, when we began tracking the data.
Management review and internal audit were the second
and third most common orms o detection, uncovering
15% and 14% o rauds, respectively. It is also noteworthy
that 11% o rauds were detected through channels that
lie completely outside o the traditional anti-raud control
structure: accident, police notication and conession. In
other words, 11% o the time, the victim organization ei-
ther had to stumble onto the raud or be notied o it by a
third party in order to detect it.
Dcn Fraud scm
Initia detetion o Opationa Fras
Respondents to our survey
were asked to identify how the
frauds were rst discovered.
Three times as many frauds in
our study were uncovered by a
tip as by any other method.
Frauds are much more likely to bedetected by tips than by any other method.
0% 10% 20% 30% 40% 50%
IT Controls
Confession
Notified by PoliceSurveillance/Monitoring
External Audit
Document Examination
Account Reconciliation
By Accident
Internal Audit
Management Review
Tip 40.2%
15.4%
13.9%
8.3%
6.1%
5.2%
4.6%
2.6%
1.8%
1.0%
0.8%
Percent of Cases
Dete
ctionMethod
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surc t
Not surprisingly, employees were the most common
source o raud tips. However, customers, vendors, com-
petitors and acquaintances (i.e., non-company sources)
provided at least 34% o raud tips, which suggests that
raud reporting policies and programs should be publi-
cized not only to employees, but also to customers, ven-
dors and other external stakeholders.
imac Annymu Rrng
Mcanm (hln)
While tips have consistently been the most common way
to detect raud, the impact o tips is, i anything, understat-
ed by the act that so many organizations ail to implement
raud reporting systems. Such systems enable employees
to anonymously report raud or misconduct by phone or
through a web-based portal.6 The ability to report raud
anonymously is key because employees oten ear making
reports due to the threat o retaliation rom superiors ornegative reactions rom their peers. Also, most third-party
hotline systems oer programs to raise awareness about
how to report misconduct. Consequently, one would ex-
pect that the presence o a raud hotline would enhance
raud detection eorts and oster more tips.
This turns out to be true. As seen on page 18, the pres-
ence o raud hotlines correlated with an increase in the
number o cases detected by a tip. In organizations that
had hotlines, 47% o rauds were detected by tips, while
in organizations without hotlines, only 34% o cases were
detected by tips. This is important because tips have
repeatedly been shown to be the most eective way to
catch raud. The better an organization is at collecting andresponding to raud tips, the better it should be at detect-
ing raud and limiting losses.
In 67% o the cases where there was an anonymous
tip, that tip was reported through an organizations raud
hotline. This strongly suggests that hotlines are an eec-
tive way to encourage tips rom employees who might oth-
erwise not report misconduct. Perhaps most important, as
noted on page 43, organizations that had raud hotlines su-
ered much smaller raud losses than organizations without
hotlines. Those organizations also tended to detect rauds
seven months earlier than their counterparts.
6For simplicitys sake, we will reer to all reporting mechanisms as hotlines in this study.
0%
10%
20%
30%
40%
50%
Perp
etrator's
Acquaintan
ce
Compe
titor
Shar
eholde
r/
Owne
rVe
ndor
Anon
ymou
s
Custom
er
Employ
ee
49.2%
17.8%
13.4%12.1%
3.7%2.5% 1.8%
Source of Tips
P
ercentofTips
Sore o Tips
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Dcn Md Bad n organzan ty
The chart on page 19 shows how rauds were detected based on the victims organization type. We see that privately
owned companies tended to have the ewest rauds detected by tip and the most rauds caught by accident, both o
which were also true in our 2008 study. Publicly held companies tended to detect more rauds by management review
and internal audit than their counterparts. Government agencies had the highest rate o detection by tips and had a pro-
portionately high rate o rauds caught through external audit.
Dcng Fraud n small BunSmall businesses historically tend to suer disproportionately high occupational raud losses, according to our previ-
ous reports. The trend was not as pronounced in this study as in past years, but we still saw that 31% o all occupa-
tional rauds were committed against small businesses (the highest rate o any category) and the median loss in those
schemes was $155,000 (see page 29). One reason that small businesses are particularly good targets or occupational
raud is that they tend to have ar ewer anti-raud controls than larger organizations (see page 39).
Dcn Fraud scm
Impat o Hotines
0% 10% 20% 30% 40% 50%
Organizations With Hotlines
Organizations Without Hotlines
Confession
Notified by Police
IT Controls
External Audit
Surveillance/Monitoring
Document Examination
By Accident
Account Reconcilliation
Management Review
Internal Audit
Tip
Percent of Cases
DetectionMethod
47.1%
33.8%
15.7%
15.7%
16.5%
11.2%
4.6%
4.7%
7.8%
3.7%
1.4%
3.0%
1.0%
0.9%
1.4%
2.2%
2.3%
1.1%
0.4%
6.5%
7.3%
11.9%
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When we look at how small businesses detect rauds, it is apparent that they catch a much lower proportion o schemes
through tips or internal audits than larger organizations. According to the chart on page 20, only 33% o small business
rauds are detected by a tip, and only 8% are detected by an internal audit. Additionally, a relatively large percentageo rauds are caught by accident at small companies nearly twice as many as at larger organizations. Many o these
discrepancies are likely due to the low rates o control implementation at small businesses.
Initia detetion Metho y Organization Type
0% 10% 20% 30% 40% 50%
Government
Public Company
Private Company
Not-for-Profit
IT Controls
Confession
Notified by Police
Surveillance/Monitoring
External Audit
Document Examination
Account Reconcilliation
By Accident
Internal Audit
Management Review
Tip
Percent of Cases
DetectionMethod
43.2%
13.0%
10.7%
6.5%
8.9%
6.5%
6.5%
1.2%
1.8%
1.2%
0.6%
15.4%
11.6%
11.2%
8.2%
6.0%
5.2%
2.6%
2.5%
1.0%
0.5%
17.6%
16.7%
6.8%
3.9%
5.0%
2.3%
3.0%
1.2%
1.1%
1.2%
11.6%
15.1%
5.3%
4.6%
3.9%
7.4%
2.8%
1.4%
1.4%
0.4%
35.8%41.1%
46.3%
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Dcn Fraud scm
Initia detetion o Fras in Sma bsinesses
Dcn occuanal Fraud Bad n Rgn
The ollowing charts show how rauds were detected based on the region in which they occurred.7 In every region, tips
were responsible or detecting the most occupational rauds by a wide margin. The percentage o cases detected by tips
ranged rom a high o 50% (in Arica) to a low o 38% (in the United States). In all but two regions, management review
and internal audit were the second and third most common means o detection, ollowing tips.
detetion in the unite States 1,001 cases
7See Appendix or a listing o countries included in each region.
0% 10% 20% 30% 40% 50%
100+ Employees
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detetion in Asia 293 cases
detetion in Erope 155 cases
0% 10% 20% 30% 40% 50%
IT Controls
Notified by PoliceConfession
Surveillance/Monitoring
Document Examination
Account Reconciliation
External Audit
By Accident
Management Review
Internal Audit
Tip 42.3%
11.3%
14.3%
8.9%
5.5%
4.4%
5.8%
2.7%
1.7%
2.4%
0.7%
Percent of Cases
D
etectionMethod
0% 10% 20% 30% 40% 50%
Confession
IT Controls
Notified by Police
Surveillance/Monitoring
External Audit
Document Examination
Account Reconciliation
By Accident
Management Review
Internal Audit
Tip 40.0%
16.1%
17.4%
6.5%
5.8%
5.2%
3.9%
3.2%
1.3%
0.0%
0.6%
Percent of Cases
DetectionMethod
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Dcn Fraud scm
detetion in Aria 111 cases
detetion in canaa 97 cases
0% 10% 20% 30% 40% 50%
IT Controls
ConfessionDocument Examination
External Audit
Notified by Police
Surveillance/Monitoring
Account Reconciliation
By Accident
Internal Audit
Management Review
Tip 49.5%
11.7%
9.9%
9.0%
6.3%
0.9%
1.8%
5.4%
4.5%
0.9%
0.0%
Percent of Cases
D
etectionMethod
0% 10% 20% 30% 40% 50%
IT Controls
Confession
Notified by Police
Surveillance/Monitoring
External Audit
Document Examination
By Accident
Account Reconciliation
Internal Audit
Management Review
Tip 46.4%
15.5%
12.4%
5.2%
6.2%
4.1%
4.1%
4.1%
1.0%
1.0%
0.0%
Percent of Cases
DetectionMethod
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detetion in centra/Soth Ameria an the cariean 70 cases
detetion in Oeania 40 cases
0% 10% 20% 30% 40% 50%
IT Controls
By AccidentNotified by Police
Confession
Surveillance/Monitoring
Document Examination
Account Reconciliation
Internal Audit
External Audit
Management Review
Tip 44.3%
14.3%
10.0%
0.0%
8.6%
4.3%
12.9%
4.3%
0.0%
1.4%
0.0%
Percent of Cases
D
etectionMethod
0% 10% 20% 30% 40% 50%
IT Controls
Confession
Notified by Police
Document Examination
External Audit
Account Reconciliation
Surveillance/Monitoring
Internal Audit
By Accident
Management Review
Tip 45.0%
20.0%
10.0%
12.5%
2.5%
0.0%
2.5%
7.5%
0.0%
0.0%
0.0%
Percent of Cases
DetectionMethod
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Ggracal Lcan organzan
As mentioned previously, or the rst time in the history
o our research on occupational raud, we opened up our
study to include raud cases investigated by CFEs out-
side the United States. As a result, the cases discussed in
this Report represent rauds perpetrated in 106 countries
around the world. We received inormation on the location
o 1,797 o the cases that were reported to us. O these,
43% occurred outside the United States, providing us with
a true insight into the global plague o occupational raud.
The chart below shows the number and median loss o
the cases reported to us, broken down by region. For vic-
tim organizations with locations in more than one coun-
try, we asked survey participants to choose the location
where the primary perpetrator was located. For example,
a raud perpetrated at a European arm o a Japanese com-
pany would be classied as occurring in Europe. Similarly,
a case involving raud perpetrated at the Canadian oce
o a South American company would be considered a
raud that occurred in Canada. The regional breakdowns
on case data throughout this Report should consequently
be read within this ramework. Additionally, due to the
large number o U.S. cases reported, we separated North
America into the United States and Canada, and grouped
the remaining countries by continent.
Vcm organzan
Geographia loation o Vitim Organizations8
Rgn Numbr Ca prcn Ca Mdan L (n U.s. dllar)
United States 1,021 56.8% $105,000
Asia 298 16.6% $274,000
Europe 157 8.7% $600,000
Arica 112 6.2% $205,000
Canada 99 5.5% $125,000
Central/South America and the Caribbean 70 3.9% $186,000
Oceania 40 2.2% $338,000
8See Appendix or a listing o countries included in each region.
As part of our survey, we asked
each respondent to provide
demographic information
about the organization that
was defrauded.
Small organizations are particularlyvulnerable to fraud.
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The ollowing tables illustrate the requency o the 11 occupational raud schemes nancial statement raud, corrup-
tion and the nine asset misappropriation sub-schemes or each region.9
unite States 1,021 cases
scmNumbr
Ca
prcn
Ca
Billing 282 27.6%
Corruption 224 21.9%
Check Tampering 173 16.9%
Skimming 165 16.2%
Non-Cash 160 15.7%
Expense Reimbursements 154 15.1%
Cash on Hand 117 11.5%
Payroll 108 10.6%
Cash Larceny 98 9.6%
Financial Statement Fraud 44 4.3%
Register Disbursements 25 2.4%
Erope 157 cases
scm Numbr Ca prcn Ca
Corruption 79 50.3%
Billing 41 26.1%
Non-Cash 31 19.7%
Expense Reimbursements 24 15.3%
Cash on Hand 23 14.6%
Skimming 17 10.8%
Cash Larceny 12 7.6%
Financial Statement Fraud 10 6.4%
Payroll 10 6.4%
Register Disbursements 7 4.5%
Check Tampering 5 3.2%
Asia 298 cases
scmNumbr
Ca
prcn
Ca
Corruption 152 51.0%
Billing 56 18.8%
Non-Cash 55 18.5%
Expense Reimbursements 43 14.4%
Skimming 38 12.8%
Cash on Hand 34 11.4%
Cash Larceny 26 8.7%
Financial Statement Fraud 21 7.0%
Check Tampering 21 7.0%
Payroll 12 4.0%
Register Disbursements 6 2.0%
Aria 112 cases
scm Numbr Ca prcn Ca
Corruption 55 49.1%
Billing 38 33.9%
Non-Cash 24 21.4%
Expense Reimbursements 19 17.0%
Cash on Hand 16 14.3%
Cash Larceny 15 13.4%
Skimming 13 11.6%
Check Tampering 11 9.8%
Payroll 6 5.4%
Register Disbursements 3 2.7%
Financial Statement Fraud 2 1.8%
9The sum o percentages in these tables exceeds 100% because several casesinvolved schemes rom more than one category.
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corrption cases y Region
Rgn Numbr Crrun Ca prcn all Ca n Rgn Mdan L
Asia 152 51.0% $330,000
Europe 79 50.3% $1,000,000
Arica 55 49.1% $208,000
Central/South America and the Caribbean 33 47.1% $250,000
Oceania 16 40.0% $800,000
United States 224 21.9% $175,000
Canada 21 21.2% $163,000
Vcm organzan
canaa 99 cases
scmNumbr
Ca
prcn
Ca
Billing 21 21.2%
Corruption 21 21.2%
Expense Reimbursements 20 20.2%
Check Tampering 17 17.2%
Non-Cash 15 15.2%
Payroll 12 12.1%
Skimming 12 12.1%
Cash Larceny 10 10.1%
Cash on Hand 9 9.1%
Register Disbursements 8 8.1%
Financial Statement Fraud 2 2.0%
Oeania 40 cases
scmNumbr
Ca
prcn
Ca
Corruption 16 40.0%
Non-Cash 12 30.0%
Billing 11 27.5%
Check Tampering 7 17.5%
Skimming 5 12.5%
Cash on Hand 4 10.0%
Expense Reimbursements 4 10.0%
Cash Larceny 3 7.5%
Payroll 2 5.0%
Register Disbursements 1 2.5%
Financial Statement Fraud 1 2.5%
centra/Soth Ameria an the cariean 70 cases
scmNumbr
Ca
prcn
Ca
Corruption 33 47.1%
Billing 20 28.6%
Non-Cash 13 18.6%
Cash Larceny 10 14.3%
Skimming 9 12.9%
Cash on Hand 8 11.4%
Expense Reimbursements 8 11.4%
Financial Statement Fraud 7 10.0%
Check Tampering 6 8.6%
Payroll 3 4.3%
Register Disbursements 1 1.4%
Crrun Ca by Rgn
We compared the proportion and cost o cases involving
corruption among the regional categories in our study. The
results are presented in the ollowing table.
Readers should keep in mind that this data does not neces-
sarily refect overall corruption levels within each region; it
only refects the specic raud cases that were investigated
and reported to us by the CFEs who took part in our study.
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ty organzan
More than 40% o victim organizations in our study were privately owned businesses, and nearly one-third were publicly
traded companies, meaning that almost three-quarters o the victims represented in our study came rom or-prot enter-
prises. Sixteen percent o the rauds reported to us occurred at government agencies. Not-or-prot organizations were the
least represented category, with less than 10% o rauds taking place at these entities.
In addition to experiencing the most rauds, private and public companies were also victim to the costliest schemes in
our study; the median loss or the cases at these businesses was $231,000 and $200,000, respectively (see page 28).
In contrast, the losses experienced by government agencies and not-or-prot organizations were about hal as much.
Government agencies had a median loss o $100,000, while not-or-prots lost a median o $90,000.
Organization Type o Vitim Freqeny
0% 10% 20% 30% 40% 50%
2010
2008
Not-for-Profit
Government
Public Company
Private Company39.1%
28.4%
32.1%
14.3%
42.1%
16.3%
18.1%
9.6%
Percent of Cases
TypeofVictimO
rganization
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Vcm organzan
Organization Type o Vitim Meian loss
Continuing the trend observed in our prior studies, small
organizations those with ewer than 100 employees
suered the greatest percentage o the rauds in our 2010
study, accounting or more than 30% o the victim orga-
nizations. However, the variation between size categories
is relatively small, with 23% o victims having between
100 and 999 employees, 26% having 1,000 to 9,999 em-
ployees and 21% having more than 10,000 employees.
This relatively small disparity contrasts with our previous
studies, in which small organizations were involved in a
much higher percent o rauds than any other category.
Additionally, our research has historically shown that
smaller organizations suer disproportionately large loss-
es due to occupational raud. Organizations with ewer
than 100 employees experienced the greatest median
loss o all categories o victim organizations in our 2008
study. The same was true in our 2006 study. However,
that was not the case when we looked at the ull body
o data rom our current survey. Consequently, we under-
took additional analyses to see what eect, i any, the in-
clusion o cases rom countries outside the United States
had on these ndings.
sz organzan
$0 $50,000 $100,000 $150,000 $200,000 $250,000 $300,000
2010
2008
Not-for-Profit
Government
Public Company
Private Company$231,000
$278,000
$200,000
$142,000
$100,000
$100,000
$109,000
$90,000
Median Loss
TypeofVic
timO
rganization
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Size o Vitim Organization Freqeny
Size o Vitim Organization Meian loss
$0 $50,000 $100,000 $150,000 $200,000
2010
2008
10,000+
1,000 9,999
100 999
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Size o Vitim Organizations Meian loss y Region
Md Fraud n small Bun
Because the challenges aced by small businesses in
combating occupational raud are numerous and unique,
it is helpul to know the types o rauds that are most
prevalent within these organizations. Such observations
may help small businesses target their limited resources
to those areas that pose the greatest risk.
O course, the specic risks aced by any organization are
largely dependent on its particular industry, operating envi-
ronment, processes, culture and many other actors. None-
theless, examining which raud schemes are most com-
monly perpetrated at small companies can aid us in better
understanding the raud issues aced by these businesses.
Sma bsinesses(
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Vcm organzan
As the chart below illustrates, check tampering schemes were much more common at small organizations than at all
other entities. Skimming and payroll rauds were also more common in small organizations. These trends stand to rea-
son, as the unctions aected by such schemes the check writing, cash collection and payroll unctions, respectively
are more likely to be perormed by a single individual, such as a bookkeeper, and are oten subject to less oversight
within a small organization than in a large company where duties are more segregated and authorization o transactions
is more ormalized. In contrast, although corruption schemes were the third most common raud scheme aced by small
businesses, they were less requent within small companies than in bigger organizations.
Methos o Fra y Size o Vitim Organization
0% 5% 10% 15% 20% 25% 30% 35% 40%
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An-Fraud Cnrl a small Bun
We have long hypothesized that many small companies are particularly susceptible to raud at least partially due to the
limited resources they devote to anti-raud controls. To test this theory, we compared the presence o anti-raud controls at
those companies with ewer than 100 employees to the controls at companies with more than 100 employees. Our ndings
conrm what we suspected: The small companies in our study did indeed have ewer controls in place than the larger orga-
nizations, a actor that may contribute to the disproportionate impact o raud on these companies. While discrepancies in
levels o certain controls are somewhat expected given the associated costs or resources required to enact them, the gap
between controls in small businesses as opposed to larger organizations is striking. For example, it would be expected that
small businesses would have a lower rate o external audits and that ewer small companies would have a ormal internal
audit or raud examination unction. But even less expensive controls were oten absent in small businesses. While 64% o
large companies had some sort o management review o controls, processes, accounts or transactions, less than hal as
many small businesses had the same type o monitoring in place. Likewise, ormal codes o conduct and anti-raud policies
cost very little to implement, but serve as an eective way to make a clear and explicit statement against raudulent and
unethical conduct within an organization. Yet only 41% and 16% o small businesses had these policies (respectively) in
place when the raud occurred numbers dwared by the 83% and 50% rates o larger organizations.
Perhaps most concerning is that only 15% o small businesses had a hotline in place, compared to 64% o larger orga-
nizations. As previously discussed, our research shows that hotlines are consistently the most eective raud detection
method. Further, as discussed on page 43, the median loss or rauds at companies with hotlines was 59% smaller than
the median loss or rauds at organizations without such a mechanism. Arguably, enacting hotlines would go a long wayin helping small-business owners protect their assets rom dishonest employees.
Freqeny o Anti-Fra contros y Size o Vitim Organization
0% 20% 40% 60% 80% 100%
100+ Employees
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Management certifationo Finania Statements
Rgn prcn Ca
Arica 68.8%
Canada 65.7%
Oceania 65.0%
Asia 62.8%
Europe 62.4%
United States 56.0%
Central/South America and Caribbean 51.4%
Inepenent Ait committee
Rgn prcn Ca
Arica 63.4%
Canada 59.6%
Oceania 57.5%
Asia 54.7%
Central/South America and Caribbean 54.3%
Europe 54.1%
United States 50.8%
Empoyee Spport Programs
Rgn prcn Ca
Canada 57.6%
United States 54.8%
Oceania 45.0%
Arica 38.4%
Central/South America and Caribbean 30.0%
Europe 28.0%
Asia 22.5%
Fra Training or Empoyees
Rgn prcn Ca
United States 42.7%
Arica 39.3%
Europe 37.6%
Asia 37.2%
Central/South America and Caribbean 32.9%
Canada 29.3%
Oceania 22.5%
Management Review
Rgn prcn Ca
Asia 59.4%
Europe 54.8%
Canada 53.5%
Arica 52.7%
Oceania 52.5%
United States 51.6%
Central/South America and Caribbean 50.0%
Hotine
Rgn prcn Ca
Central/South America and Caribbean 52.9%
United States 52.0%
Arica 47.3%
Europe 45.9%
Asia 43.3%
Canada 41.4%
Oceania 25.0%
Fra Training orManagers/Exetives
Rgn prcn Ca
United States 44.5%
Arica 41.1%
Asia 40.9%
Europe 37.6%
Central/South America and Caribbean 37.1%
Canada 30.3%
Oceania 25.0%
Anti-Fra Poiy
Rgn prcn Ca
Arica 49.1%
United States 38.7%
Central/South America and Caribbean 38.6%
Canada 38.4%
Asia 36.6%
Europe 36.3%
Oceania 32.5%
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Meian loss base on Presene o Anti-Fra controsCnrl17 prcn Ca imlmnd Cnrl n plac Cnrl N n plac prcn Rducn
Hotline 48.6% $100,000 $245,000 59.2%
Employee Support Programs 44.8% $100,000 $244,000 59.0%
Surprise Audits 28.9% $97,000 $200,000 51.5%
Fraud Training or Employees 39.6% $100,000 $200,000 50.0%
Fraud Training or Managers/Execs 41.5% $100,000 $200,000 50.0%
Job Rotation/Mandatory Vacation 14.6% $100,000 $188,000 46.8%
Code o Conduct 69.9% $140,000 $262,000 46.6%
Anti-Fraud Policy 39.0% $120,000 $200,000 40.0%
Management Review 53.3% $120,000 $200,000 40.0%
External Audit o ICOFR 59.3% $140,000 $215,000 34.9%
Internal Audit/FE Department 66.4% $145,000 $209,000 30.6%
Independent Audit Committee 53.2% $140,000 $200,000 30.0%
Management Certication o F/S 58.9% $150,000 $200,000 25.0%
External Audit o F/S 76.1% $150,000 $200,000 25.0%
Rewards or Whistleblowers 7.4% $119,000 $155,000 23.2%
Similarly, we compared the duration o raud schemes at organizations with and without anti-raud controls. As refected
in the table below, the presence o each control correlated with a reduction in the duration o raud. We ound it interest-
ing that the controls associated with the greatest reduction in scheme lengths are not the same as the ones that had the
most impact on median loss.
dration base on Presene o Anti-Fra controsCnrl17 prcn Ca imlmnd Cnrl n plac Cnrl N n plac prcn Rducn
Management Review 53.3% 12 months 24 months 50.0%
Internal Audit/FE Department 66.4% 14 months 24 months 41.7%
External Audit o ICOFR 59.3% 15 months 24 months 37.5%
Code o Conduct 69.9% 15 months 24 months 37.5%
Surprise Audits 28.9% 12 months 19 months 36.8%
Hotline 48.6% 13 months 20 months 35.0%
Management Certication o F/S 58.9% 15 months 23 months 34.8%
Rewards or Whistleblowers 7.4% 12 months 18 months 33.3%Job Rotation/Mandatory Vacation 14.6% 12 months 18 months 33.3%
External Audit o F/S 76.1% 16 months 24 months 33.3%
Anti-Fraud Policy 39.0% 13 months 18 months 27.8%
Fraud Training or Employees 39.6% 13 months 18 months 27.8%
Fraud Training or Managers/Execs 41.5% 13 months 18 months 27.8%
Independent Audit Committee 53.2% 15 months 20 months 25.0%
Employee Support Programs 44.8% 15 months 18 months 16.7%
17KeY:
External Audit o F/S = Independent external audits o the organizations nancial statements
Internal Audit / FE Department = Internal audit department or raud examination department
External Audit o ICOFR = Independent audits o the organizations internal controls over nancial reporting
Management Certication o F/S = Management certication o the organizations nancial statements
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imranc Cnrl n Dcng r Lmng Fraud
Not all controls are eective against all rauds. Most control mechanisms are more likely to detect or deter some raud
schemes than others. Likewise, some perpetrators are more adept than others at circumventing particular controls, and
some controls are more susceptible to being overridden than others.
We thought it useul to examine which controls had the greatest eect on the rauds reported in our study. We asked the
CFEs who took part in our survey to rank the importance o several anti-raud controls in detecting or limiting the raud.
The ollowing chart shows the respondents opinions regarding each controls useulness.
Vcm organzan
Importane o contro in deteting or limiting Fra
18
KeY:External Audit o F/S = Independent external audits o the organizations nancial statements
Internal Audit / FE Department = Internal audit department or raud examination department
External Audit o ICOFR = Independent audits o the organizations internal controls over nancial reporting
Management Certication o F/S = Management certication o the organizations nancial statements
0% 10% 20% 30% 40% 50% 60% 70% 80%
Not at all Important
Somewhat Important
Very Important
External Audit of F/S
External Auditof ICOFR
Rewards forWhistleblowers
Hotline
Job Rotation/Mandatory Vacation
Surprise Audits
Management Review
Internal Audit/FE Department
Percent of Respondents
Control18
60.8%
50.3%
49.0%
42.0%
41.3%
38.3%
36.0%32.4%
29.4%
31.5%
40.7%
45.4%
27.9%23.0%
24.5%
17.9%
25.3%21.4%
25.7%
25.3%31.2%
19.3%
23.5%
19.2%
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Vcm organzan
Primary Interna contro Weakness y Size o Vitim Organization
Primary Interna contro Weakness in largest cases
0% 10% 20% 30% 40% 50%
100+ Employees
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prrar
prrar Ag
The distribution o perpetrators based on their age was similar to our 2008 study, but the 2010 perpetrators tended to be
slightly younger. Our past reports have generally shown the highest levels o raud to occur in the 3650 age range, but this
year we ound more than hal o all cases were committed by individuals between the ages o 31 and 45. Generally speak-
ing, median losses tended to rise with the age o the perpetrator, which is consistent with our prior research. The most
notable dierence between 2008 and 2010 is the losses caused by perpetrators older than 60. In each study, however,
we were dealing with ewer than 40 cases in that category. Given the small sample size, we believe this is more likely to
be an anomaly than an indication o any particular trend.
Age o Perpetrator Freqeny
0%
5%
10%
15%
20%
2010
2008
>6056 6051 5546 5041 4536 4031 3526 30
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prrar Darmn Bad n Rgn
The ollowing tables show the distribution o perpetrators based on their department or each region. In every region but
Asia, accounting departments were associated with the greatest number o rauds. Overall, the distribution o cases was
very similar regardless o region. The six highest-requency departments (accounting, operations, sales, executive/upper
management, customer service and purchasing) accounted or between 70% and 85% o the cases in every region.
unite States 913 cases
DarmnNumbr
Ca
prcn
CaAccounting 222 24.3%
Operations 189 20.7%
Executive/Upper Management 127 13.9%
Sales 120 13.1%
Customer Service 77 8.4%
Purchasing 39 4.3%
Warehousing/Inventory 36 3.9%
Finance 28 3.1%
Inormation Technology 26 2.8%
Manuacturing and Production 11 1.2%
Marketing/Public Relations 11 1.2%
Legal 7 0.8%Board o Directors 6 0.7%
Human Resources 6 0.7%
Research and Development 6 0.7%
Internal Audit 2 0.2%
Erope 146 cases
DarmnNumbr
Ca
prcn
Ca
Accounting 26 17.8%
Executive/Upper Management 23 15.8%
Operations 21 14.4%
Purchasing 13 8.9%
Sales 13 8.9%
Finance 11 7.5%
Customer Service 8 5.5%
Warehousing/Inventory 8 5.5%
Board o Directors 6 4.1%
Inormation Technology 6 4.1%
Marketing/Public Relations 5 3.4%
Research and Development 4 2.7%
Human Resources 1 0.7%
Manuacturing and Production 1 0.7%
Asia 272 cases
DarmnNumbr
Ca
prcn
CaSales 57 21.0%
Operations 42 15.4%
Accounting 41 15.1%
Executive/Upper Management 38 14.0%
Purchasing 29 10.7%
Finance 11 4.0%
Warehousing/Inventory 11 4.0%
Customer Service 9 3.3%
Board o Directors 8 2.9%
Marketing/Public Relations 8 2.9%
Human Resources 6 2.2%
Manuacturing and Production 6 2.2%Inormation Technology 4 1.5%
Internal Audit 1 0.4%
Research and Development 1 0.4%
Aria 105 cases
DarmnNumbr
Ca
prcn
Ca
Accounting 31 29.5%
Operations 13 12.4%
Finance 11 10.5%
Customer Service 9 8.6%
Executive/Upper Management 9 8.6%
Purchasing 7 6.7%
Warehousing/Inventory 6 5.7%
Human Resources 5 4.8%
Sales 5 4.8%
Inormation Technology 3 2.9%
Manuacturing and Production 3 2.9%
Board o Directors 1 1.0%
Legal 1 1.0%
Marketing/Public Relations 1 1.0%
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sal Darmn
The most common rauds in the sales department were corruption (34% o cases) and thet o non-cash assets (24%).
Fraudsters in the sales department were somewhat more likely than others to steal non-cash assets. Conversely, they
were much less likely to engage in billing schemes, check tampering or payroll raud.
Shemes committe y Perpetrators in the Saes department 225 cases25
25The sum o percentages in this chart exceeds 100% because several cases involved schemes rom more than one category.
0% 5% 10% 15% 20% 25% 30% 35%
All Cases
Sales
Payroll
Financial Statement Fraud
Check Tampering
Register Disbursements
Cash Larceny
Cash on Hand
Billing
Expense Reimbursement
Skimming
Non-Cash
Corruption
Percent of Cases
SchemeType
33.8%
23.6%
17.5%
32.8%
16.4%
14.5%
15.6%
15.1%
26.0%
13.8%
12.0%
12.0%
9.3%
8.0%
13.4%
4.0%
3.6%
4.8%
1.8%
8.5%
9.8%
3.0%
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Cumr srvc Darmn
Corruption was the most common orm o raud among customer service employees (22% o cases), but compared
to the distribution or all cases, we see that corruption was actually much less likely to occur in customer service than
elsewhere. Conversely, skimming, thet o cash on hand and raudulent register disbursements were more likely to occur
in customer service than in other areas o the organization.
Shemes committe y Perpetrators in the cstomer Servie department 120 cases27
27The sum o percentages in this chart exceeds 100% because several cases involved schemes rom more than one category.
0% 5% 10% 15% 20% 25% 30% 35%
All Cases
Customer Service
Payroll
Financial Statement Fraud
Expense Reimbursement
Register Disbursements
Check Tampering
Billing
Cash Larceny
Non-Cash
Cash on Hand
Skimming
Corruption
Percent of Cases
S
chemeType
21.7%
19.2%
14.5%
32.8%
18.3%
12.0%
17.5%
17.5%
9.8%
9.2%
8.3%
26.0%
8.3%
8.3%
15.1%
3.3%
1.7%
4.8%
0.8%
8.5%
13.4%
3.0%
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prrar Crmnal and emlymn
hry
prrar Crmnal Backgrund
Only 7% o the raud perpetrators in our study had been
previously convicted o a raud-related oense, which
was virtually identical to our nding in 2008. Eighty-six
percent had never been charged with or convicted o a
prior oense. The low rate o prior convictions suggests
that criminal background checks may have some eect in
preventing raud, but the eect is probably limited.
prrar emlymn Backgrund
In addition to criminal history, past employment issues
may indicate that an employee is more likely to engage
in raudulent conduct in the uture. O the respondents in
our survey, 791 were able to provide inormation about
the perpetrators prior employment history. Among those
cases, about 8% o perpetrators had been previously
punished and 10% had been previously terminated or
raud-related conduct.
Bavral Rd Flag Dlayd by
prrar
While a raud is ongoing, the perpetrator oten displays
certain behaviors or characteristics that might indicate he
or she has a heightened risk o committing raud. On their
own, these behavioral red fags do not prove an individual
is engaged in a raud, but they should raise warning sig-
nals to the individuals co-workers and managers, as well
as the organizations anti-raud sta. When these red fags
exist alongside other indicators o misconduct, this can be
a strong clue that something is wrong. As discussed earlier
in this report, occupational rauds oten last or months or
years beore they are caught, so the ability to detect rauds
as early as possible can have a big eect in limiting losses.
0%
20%
40%
60%
80%
100%2010
2008
Charged But
Not Convicted
Prior
Convictions
Never Charged
or Convicted
85.7%87.4%
6.7% 6.8% 7.7%5.7%
Criminal Background
PercentofCases
0%
20%
40%
60%
80%
100% 2010
2008
PreviouslyPunished
PreviouslyTerminated
Never Punishedor Terminated
82.4% 82.6%
9.5%12.3%
8.1%5.1%
Employment Background
PercentofCases
Perpetrators crimina bakgron
Perpetrators Empoyment bakgron
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prrar
We presented survey respondents with a list o common behavioral red fags and asked them to identiy which o these
warning signs had been displayed by the perpetrator prior to detection o the raud. As shown in the chart below, the most
common red fags displayed by perpetrators were living beyond nancial means (43% o cases), experiencing nancial
diculties (36%), excessive control issues with regard to their jobs (23%) and an unusually close association with vendors
or customers (22%). This distribution is very similar to what we ound in our 2008 study. As we continue to track this
data in uture studies, we hope to be able to identiy consistent relationships between behavioral warning signs and
the occurrence o occupational raud. Ideally, this data will help organizations build better raud-detection programs that
incorporate behavioral data in addition to more standard anti-raud controls.
behaviora Re Fags o Perpetrators29
29The sum o percentages in this chart exceeds 100% because in many cases perpetrators displayed more than one behavioral red fag.
0% 10% 20% 30% 40% 50%
2010
2008
Complained about lack of authority
Excessive family/peer pressure for success
Instability in life circumstances
Past legal problems
Excessive pressure from within organization
Complained about inadequate pay
Past employment-related problems
Refusal to take vacations
Addiction problems
Irritability, suspiciousness or defensiveness
Divorce/family problems
Wheeler-dealer attitude
Unusually close association with vendor/customer
Control issues, unwillingness to share duties
Financial difficulties
Living beyond means 43.0%38.6%
36.4%
34.1%
22.6%
18.7%
22.1%
15.2%
19.2%
20.3%
17.6%
17.1%
14.1%
13.6%
11.9%
13.3%
10.2%
6.8%
9.3%
7.9%
7.9%
7.3%
7.5%
6.5%
6.3%
8.7%
5.6%
4.9%
5.1%
4.2%
4.6%
3.6%
Percent of Cases
BehavioralRed
Flag
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Red Flags Based on peretrators poston
The behavioral indicators that a raud perpetrator displays can vary depending on a number o actors. The ollowing
chart shows the distribution o red fags based on the perpetrators level o authority. Among employee-level raudsters,
the most common behavioral red fag was nancial diculties, which was present in nearly hal o all employee raud
cases. Because employee-level raudsters generally have lower incomes than managers or owners/executives, we would
expect their motivation or committing raud to more oten be based on an immediate, pressing nancial need, which
explains why this red fag shows up so oten. While nancial diculty was still requently cited in cases involving manag-
ers and owners/executives, it occurred much less oten. Conversely, owners/executives and managers were much more
likely than employees to display control issues, to have unusually close associations with vendors or customers or to
exhibit a wheeler-dealer attitude. Each o these red fags tends to refect the authority level o owners/executives and
managers, who are in a better position than employees to infuence organizational decision-making, arrange deals with
outside parties and exert their control over the direction or tone o the organization.
behaviora Re Fags o Perpetrators base on Position30
30The sum o percentages in this chart exceeds 100% because in many cases perpetrators displayed more than one behavioral red fag.
0% 10% 20% 30% 40% 50%
Owner/Executive
Manager
Employee
Complained about lack of authority
Excessive family/peer pressure for success
Instability in life circumstances
Past legal problems
Excessive pressure from within organization
Complained about inadequate pay
Past employment-related problems
Refusal to take vacations
Addiction problems
Irritability, suspiciousness or defensiveness
Divorce/family problems
Wheeler-dealer attitude
Unusually close association with vendor/customer
Control issues, unwillingness to share duties
Financial difficulties
Living beyond means40.7%
43.8%
30.7%26.1%
25.8%33.0%
29.8%
35.2%
24.9%
21.0%
19.3%16.4%16.5%
9.8%16.9%16.5%
12.1%12.7%
11.6%10.0%
7.7%
11.1%7.6%8.4%
9.8%6.8%7.3%
4.8%9.9%
8.4%
6.3%5.9%
7.7%
6.6%4.3%5.7%
5.8%4.3%5.7%
6.7%2.7%
2.6%
10.7%
15.2%
12.1%
10.7%
48.3%
47.6%
Percent of Cases
BehavioralRed
Flag
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Mdlgy
The2010 Report to the Nations on Occupational Fraud and
Abuse is based on the results o an online survey distrib-
uted to 22,927 Certied Fraud Examiners (CFEs) in Octo-
ber 2009. As part o the survey, respondents were asked
to provide a detailed narrative o the single largest raud
case they had investigated that met our explicit criteria:
The case must have involved occupational raud1.(dened as internal raud, or raud committed bya person against the organization or which he or
she works).
The investigation must have occurred between2.January 2008 and the time o survey participation.
The investigation must have been completed.3.
The CFE must have been reasonably sure the4.perpetrator(s) was/were identied.
Respondents were also presented with 87 questions to
answer. These questions covered particular details o the
scheme, including inormation about the perpetrator, the
victim organization and the methods o raud employed, as
well as raud trends in general. Overall, we received 1,939
responses to the survey, 1,843 o which were usable or
purposes o this Report. The data contained herein is based
solely on the inormation provided in these 1,843 cases.
W prvdd Daa?
We sent the survey to all CFEs in good standing at the
time o the survey launch. We asked respondents to pro-
vide certain inormation about their proessional experi-
ence and qualications so that we could gather a uller
understanding o who was involved in investigating the
rauds reported to us as part o our research.
prmary occuan
More than hal o the CFEs who participated in our study
identied themselves as either raud examiners or internal
auditors. Another 12% stated that they are accountants,
and just over 7% indicated they work as law enorcement
ocers.
The 2010 Report to the Nations
on Occupational Fraud and
Abuse is based on the results of
an online survey distributed
to 22,927 Certied Fraud
Examiners (CFEs) in late 2009.
The data in this study is based on 1,843cases of occupational fraud that werereported by CFEs.
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Andx
Brakdwn Ggrac Rgn by Cunry
Aria 112 cases
CunryNumbr
Ca
Cameroon 1
Democratic Republic o the Congo 1
Egypt 5
Ethiopia 1
Ghana 4Guinea 1
Kenya 7
Liberia 1
Malawi 1
Mauritius 2
Mozambique 2
Nigeria 21
Republic o the Congo 1
Senegal 1
South Arica 47
Sudan 1
Tanzania 4
Tunisia 1
Uganda 5
Zambia 2
Zimbabwe 3
Asia 298 cases
CunryNumbr
Ca
Aghanistan 1
Bahrain 1
Cambodia 2
China 62
Cyprus 3India 37
Indonesia 27
Iran 1
Iraq 1
Japan 16
Jordan 4
Kuwait 3
Kyrgyzstan 1
Lebanon 4
Malaysia 22
Oman 4
Pakistan 8
Philippines 16
Qatar 5
Saudi Arabia 9
Singapore 7
South Korea 5
Sri Lanka 2
Taiwan 4
Tajikistan 1
Thailand 2
Turkey 20
Turkmenistan 2
United Arab Emirates 27
Vietnam 1
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centra/Soth Ameria anthe cariean 70 cases
CunryNumbr
Ca
Argentina 7
Bahamas 1
Barbados 1
Belize 1
Bolivia 1
Brazil 12
Chile 1
Colombia 3
Costa Rica 1
Dominican Republic 2
Grenada 1
Honduras 1
Jamaica 4
Mexico 20
Nicaragua 2
Panama 1
Peru 3
Saint Lucia 1
Saint Vincent and the Grenadines 2
Trinidad and Tobago 4
Venezuela 1
Oeania 40 cases
CunryNumbr
Ca
Australia 29
Fiji 2
Micronesia 1
New Zealand 8
Erope 157 cases
CunryNumbr
Ca
Austria 3
Belgium 9
Bulgaria 3
Czech Republic 5
Estonia 1
Finland 3
France 1
Germany 19
Greece 6
Hungary 3
Ireland 1
Italy 7
Kosovo 1
Liechtenstein 1
Luxembourg 1
Montenegro 1
Netherlands 14
Poland 9
Portugal 2
Romania 5
Russia 18
Serbia 1
Slovakia 1
Slovenia 1
Spain 8
Switzerland 4
Ukraine 1
United Kingdom 28
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5. ar r rk mn rrm rcvly ny n mg cmny vlnrbl
nrnl n xrnl r?
6. ar rng n