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White Collar CrimeExplore how society defines this as a Crime. How does this topic become socially constructed as a crime? What two theoretical perspectives help you to understand this phenomenon? What are the issues that are at stake in this topic? What intervention or suppression measures have been taken by society to address this issue? Please make sure to substantiate the materials with facts and not just opinions. Please use the peer review or academic references throughout your paper that I provided as well as any other outside sources. Double-spaced, Minimum of 7 pages, apa format, times new roman
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Business Horizons (2023) 66, 573e583
Available online at www.sciencedirect.com
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Profiling the modern white-collar
criminal: An overview of Utah’s
white-collar crime registry
Cedric Michel a,*, Bella L. Galperin b
a
Department of Criminology & Criminal Justice, The University of Tampa, 401
W. Kennedy Boulevard, Tampa, FL 33606-1490, USA
b
Department of Management & Entrepreneurship, The University of Tampa, 401
W. Kennedy Boulevard, Tampa, FL 33606-1490, USA
KEYWORDS
White-collar crime;
Profiling;
Crime registries
Abstract White-collar crime continues to be a central challenge for individuals,
organizations, and consumers. Due to the substantial social harm caused by
white-collar crime, there has been a focus on prevention and detection strategies,
including profiling offenders in an effort to understand their motives, targets, and
modus operandi. Despite advances in profiling white-collar offenders, these profiles
have relied heavily on the same thirty-year-old, federal-level database limited to
major urban areas, which may obscure changes in the sociodemographics of
white-collar offenders. Based on Utah’s more recent White-Collar Crime Registry,
this article seeks to better identify the profiles of modern white-collar offenders.
Our findings uncovered three distinct types of white-collar criminals: The Tech
Scammer, the Ponzi Schemer, and the Insurance Fraudster. Despite the registry’s
limitations as a database, we offer recommendations regarding the prevention
and detection of such offenders. With a more in-depth understanding of the modern white-collar criminal, managers will be better positioned to manage whitecollar crime in the workplace.
ª 2022 Kelley School of Business, Indiana University. Published by Elsevier Inc. All
rights reserved.
“Government has coddled, accepted, and
ignored white-collar crime for too long. It is
time the nation woke up and realized that it’s
not the armed robbers or drug dealers who
cause the most economic harm, it’s the white* Corresponding author
E-mail addresses: [email protected] (C. Michel), bgalperin@
ut.edu (B.L. Galperin)
collar criminals living in the most expensive
homes who have the most impressive resumes
who harm us the most. They steal our pensions,
bankrupt our companies, and destroy thousands
of jobs, ruining countless lives.”
d Harry Markopolos, American former securities industry executive and forensic accounting & financial
fraud investigator
https://doi.org/10.1016/j.bushor.2022.11.003
0007-6813/ª 2022 Kelley School of Business, Indiana University. Published by Elsevier Inc. All rights reserved.
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1. The white-collar criminal: Another
look
White-collar crime is a serious social issue that
costs American society anywhere from $426 billion
to $1.7 trillion each year (Flynn, 2022). Wellpublicized cases, such as the Enron and WorldCom fiascos or the infamous Madoff Ponzi scheme,
have made white-collar crime a household term in
America. Bernie Madoffdhaving a reputation as a
successful and influential financier, broker, financial consultant, and generous philanthropistdbuilt
the largest Ponzi scheme in US history, which
conned thousands of investors.
First coined in 1939 by criminologist Edwin
Sutherland, the term white-collar crime has often
been associated with the educated and affluent.
Media representations have further popularized
the image of white-collar offenders as upperclass, middle-aged white men (e.g., Oliver
Stone’s movie Wall Street, inspired by the reallife Ivan Boesky/Michael Milken scandal (Moohr,
2015) or Martin Scorsese’s Wolf of Wall Street,
which examined similar issues by depicting a
corrupt stockbroker). Such portraits are consistent with Edwin Sutherland’s (1949, p. 272) original definition of white collar as “a crime
committed by a person of respectability and high
social status in the course of his/her occupation.”
Generally, white-collar crimes tend to be financially motivated and nonviolent, occurring in the
office environment. Some examples of these
crimes include Ponzi schemes, insider trading,
fraud, bribery, forgery, money laundering,
embezzlement, cybercrime, identity theft, and
copyright infringement.
Nevertheless, traditional beliefs about sociodemographic characteristics (i.e., upper-class,
middle-aged white men) and the nature of whitecollar crime have been challenged. Namely, lowerlevel white-collar offenses (e.g., embezzlement
vs. Ponzi schemes) appear to be committed by:
1. Younger criminals (e.g., Lewis, 2002) compared
to traditional corporate criminals (e.g.,
Weisburd et al., 1991).
2. Women, who commit a considerable percentage of petty white-collar offenses (e.g.,
embezzlement and asset misappropriation)
compared to men (Benson & Kerley, 2000).
3. Racial minorities, who seem disproportionately overrepresented in lower-level whitecollar offenses, such as identity theft,
BUSINESS LAW & ETHICS CORNER
embezzlement, and check fraud (e.g., Copes
& Vieraitis, 2009).
4. Working- and middle-class individuals, who
represent most individuals involved in lowerlevel white-collar offenses (Benson, 2002).
Based on the literature, it appears that the
concept of white-collar crime originally proposed
by Sutherland has evolved to reflect a diverse
population. Thus, organizational scholars and
practitioners need to better understand the current profile of white-collar offenders given today’s
context. Through this article, we intend to fill the
gap by using data from an online registry for whitecollar offenders in Utah to profile these individuals
and determine which types of convicted whitecollar offenders emerge. While our results are
essentially only applicable to Utah, we are still
able to better understand white-collar offenders
and suggest prevention and detection strategies.
2. White-collar crime
While traditional crime continues to decline
across the US after almost 30 years (Grawert et al.,
2018), white-collar crime is on the rise. Theorists
suggest that the rise in white-collar crime is due to
advancing technology and globalization. In addition, greater reliance on smartphones and computers to access personal and financial information
thereby increases vulnerability to cyberattacks
worldwide (Flynn, 2022).
Although white-collar crimes differ from traditional street crimes (e.g., theft or drug dealing),
they are just as severe and detrimental to society
compared to other crimes. In line with the quote
by Harry Markopolos at the beginning of this articledwhich stresses the impact of white-collar
crimesdFriedrichs (2010) provided three factors
that further illustrate the impact of white-collar
crime: (1) its financial cost far exceeds that of
street crime; (2) it is more likely to be victimized
by white-collar crime than by street crime; and (3)
being a victim of a white-collar crime is as devastating to quality of life as street crime. Perri (2011)
argued that viewing white-collar offenders as
nonviolent is misguided. Some white-collar offenders may resort to violence, namely homicide,
to prevent their fraud schemes to be uncovered.
With respect to the breakdown of white-collar
crimes (Flynn, 2022), fraud is the most common
crime (63%), followed by embezzlement (8.59%)
and larceny/theft (7.8%). Overall, fraud is the
BUSINESS LAW & ETHICS CORNER
highest because it includes a wide range of
different white-collar crimes. Although these data
provide some insights into trends, the percentage
of white-collar crime is largely unknown due to
underreporting, lack of prosecution, and criminals
who never get caught. For example, despite an
estimated 24% of American households being
impacted by white-collar crime, 88% of victims do
not file a formal complaint (Flynn, 2022). Moreover, the definitional ambiguity of white-collar
crime creates a daunting challenge to such an
academic endeavor. What exactly constitutes
white-collar crime? What range of offenses does
white-collar crime actually encompass, and who
commits these crimes? Eighty years after its definitional inception, the concept still divides
scholars. The central question that continues in
the literature is whether to rely on offender-based
or offense-based definitions.
3. A definitional debate
The concept of white-collar criminality was publicly
introduced by Edwin Sutherland in his 1939 presidential address to a joint meeting of the American
Sociological Society and the American Economics
Association. Sutherland’s (1949, p. 272) definition
of white-collar crime by a person of respectability
and high social status in their occupation was
informed by the differential association theory
(Sutherland, 1939). The differential association
theory posits that criminal behavior is a culturally
based learning process. According to Sutherland,
privileged members of society could also succumb
to the temptation of crime through associations
with negative role models whose deviant behavior
they would rationalize and eventually mimic to
achieve desired goals. This approach stood in stark
contrast with contemporary biological explanations
that attributed crime to genetic and intellectual
deficiencies more commonly found among the
lower class. Rather provocatively, Sutherland’s
(1949) offender-based definition, which targeted
the upper-class world, quickly drew criticism for its
ideological undertones.
Shortly after Sutherland’s first foray, Tappan
(1947) called out his approach for being too dogmatic, reasoning that many business practices did
not technically violate actual legal statutes. In a
different approach in his rebuke of Sutherland,
Edelhertz (1970) considered white-collar crime to
be “democratic” and argued that it could be
committed by a lower-level employee just as well.
Consequently, a scientific investigation of fraudulent activities ought to concentrate on the characteristics of the crime, not the criminal. This
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paradigm shift established an offense-based definition less concerned with issues of respectability,
power, or privilege.
The federal government (U.S. Department of
Justice, 1989) drew from this classless revision to
define white-collar crime as:
Illegal acts which are characterized by deceit,
concealment, or violation of trust and which are
not dependent upon the application of force or
violence; individuals and organizations commit
these acts to obtain money, property, or services; to avoid the payment or loss of money or
services; or to secure personal or business
advantage.
While this approach was broad enough to include
crimes committed by corporate, political, and
financial elites, it still focused largely on middleclass and even working-class offenders, the offenses of whom were usually easier and quicker to
prosecute.
Currently, theoretical compromises between
these two definitions also exist in the literature.
Friedrichs (2010) suggested categorizing whitecollar crime by:
1. The context in which illegality occurs, including
the setting (e.g., corporation, government
agency, professional service) and the level
within the setting (e.g., individual, workgroup,
organization);
2. The status or position of the offender (e.g.,
wealthy or middle-class, CEO or employee);
3. The primary victims (e.g., general public or
individual clients);
4. The principal form of harm (e.g., economic or
physical injury); and
5. The legal classification of the act (e.g., antitrust, fraud).
One advantage of disaggregating white-collar offenders by specific typologiesdor profilesdis the
identification of several homogeneous groups with
idiosyncratic characteristics and motives. Below,
the literature on white-collar profiling is discussed.
4. White-collar offender profiles
Sutherland (1949) attributed white-collar crime to
elite members of power structures, which,
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historically, have been upper-class, middle-aged
white men. Nevertheless, research informed by
the offense-based definition shows significant
sociodemographic variation in the type of offense
categorized as white-collar. In fact, lower-level
offenses appear to be committed by a growing
number of younger and financially modest offenders, women, and racial minorities. Each demographic variable is described in greater detail
below.
With respect to age, white-collar criminals start
their criminal careers in their late 30s to 40s
(Benson & Kerley, 2000; Lewis, 2002; Weisburd
et al., 1991, 2001; Wheeler et al., 1988). That is,
compared to higher-level offenses (e.g., complex
Ponzi schemes) committed by middle-aged executives, lower-level offenses (e.g., embezzlement)
seem to have a younger age of onset (Lewis, 2002;
Weisburd et al., 1991, 2001; Wheeler et al., 1988).
This should come as no surprise since opportunities
for high-scale fraud tend to increase with
seniority.
Regarding gender, women’s participation in
white-collar crime is generally limited to lowerlevel offenses, such as embezzlement, asset
misappropriation, and identity theft (Copes &
Vieraitis, 2009; Holtfreter, 2005; Weisburd et al.,
1991, 2001). In their review of the role of female
offenders in 83 corporate crime cases,
Steffensmeier et al. (2013) concluded that the vast
majority of these women were mainly accessories
to these crimes. The systemic exclusion of females
from positions of power and their overrepresentation in roles as secretaries, managers,
and accountants has led scholars to coin the term
“pink-collar crime” to describe petty white-collar
offenses, specifically committed by women (Daly,
1989). A similar observation can be made
regarding the racial composition of white-collar
offenders. Several studies have shown a growing
involvement of African Americans in petty whitecollar crime, such as identity theft, embezzlement, and check fraud (Copes & Vieraitis, 2009;
Daly, 1989; Lewis, 2002).
Given an offense-based approach in the literature, white-collar criminals tend to be mostly
comprised of middle-class and upper-middle-class
offenders (Benson, 2002; Weisburd et al., 1991,
2001). Nevertheless, there is a growing proportion
of lower- and working-class individuals who may or
may not be employed (Copes & Vieraitis, 2009) due
to the diversity of crimes under investigation (e.g.,
auto-repair rip-offs, credit card fraud, and
internet scams).
BUSINESS LAW & ETHICS CORNER
5. Knowledge gaps in profiling
Unfortunately, the research on profiling is not
without its limitations. Dodson and Klenowski
(2016) noted that a number of previous studies
profiling white-collar offenders were based on the
same database from 30 years ago (e.g., Weisburd
et al., 1991, 2001; Wheeler et al., 1988). Unsurprisingly, similar profiles kept emerging. Moreover,
much of the data used in this literature only
focused on federal-level offenders. The dearth of
information about state-level white-collar criminals precludes any comparison between state and
federal trends, including offenders’ demographics,
offenses, and targets. Lastly, concentrating on
federal convictions in major urban areas may lead
us to underestimate certain white-collar crimes
more commonly committed in sparsely populated
suburban and rural areas (e.g., insurance fraud,
wire fraud). Therefore, the partitioning of whitecollar offenders into criterion-based typologies
would greatly benefit from more recent, statelevel data that cover different geographic locations of varying population sizes. The White-Collar
Crime Offender Registry of Utah has the potential
to help researchers address some of these
limitations.
6. Utah’s white-collar crime registry
Utah became the firstdand onlydstate to launch
an online registry. The White-Collar Crime
Offender Registry was created by the 2015 General
Session HB 378 and is mainly codified in Utah Code
xx77-42-101, et. seq. As the brainchild of Utah’s
Attorney General Sean Reyes, the registry was
established to expose individuals convicted of a
white-collar felony of the second degree or higher
and make their information public via a targeted
online search. Offenders convicted before that
date who followed all court-ordered conditions
were not added to the registry. That is, under Utah
Code x 77-42-106(3), an offender is not required to
register if the offender: (1) has complied with all
court orders made at the time of sentencing; (2)
has paid in full all court-ordered amounts of
restitution to victims; and (3) has not been convicted of any other offense for which registration
would be required.
Individuals with only one conviction are registered
for 10 years, but a second conviction increases the
registration period to 20 years. A third conviction
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results in a lifetime registration. The registry includes eight different white-collar offenses:
1. Securities fraud;
2. Theft by deception;
3. Unlawful dealing of property by fiduciary;
4. Fraudulent insurance;
5. Mortgage fraud;
6. Communications fraud;
7. Money laundering; and
8. Any pattern of unlawful activity.
Despite criticism directed at criminal registries,
including the lack of discernible deterrents and
the cost of stigmatization (LeMond, 2017), Reyes
argued that such an initiative is warranted in a
state particularly vulnerable to religious-based
affinity fraud (e.g., Utah, considering its significant Mormon population). Recent data shows that
approximately 68% of Utah’s population is Mormon
(World Population Review, 2022). In addition, Utah
investors lost over $1.5 billion in Ponzi scams over
10 years, which does not include other affinity
frauds and investment scams accounting for
another $500 million in losses to Utah residents.
For example, in 2015, an insurance agent who
belonged to the Church of Latter-day Saints was
charged with organizing a Ponzi scheme that took
more than $72 million from around 700 people
(The Atlantic, 2016). Fraud-victim losses in Utah in
2010 topped $1 billion, with popular Ponzi schemes
involving foreign currency trading, as well as
commodities and real estate investments.
The government of Utah argues that such a
program can reduce the incidence of white-collar
crime via prevention (i.e., by raising public
awareness) and deterrence (i.e., by dissuading
potential one-time white-collar offenders and
discouraging recidivism). In addition, the confidence among investors may be restored as they
will be provided with the opportunity to find out
whether the people offering the investment
appear on the registry. Listed offenders can
be identified by height, weight, eye color, hair
color, and the date they were born. Details of
the crime(s) they committed (offense type and
victims) and the conviction (year, month,
and court that rendered judgment) are also
included.
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Despite the registry’s limitations, including the
relatively small sample size and limited list of
white-collar offense categories, we believe this
database is a promising tool for better understanding white-collar crime. Although Utah relies
on an offense-based definition of white-collar
crime, the registry contains valuable information
about the sociodemographic characteristics of
state-level offenders from various geographic locations. As such, the database can address some
of the limitations observed in previous research
that sought to profile white-collar criminals. In
our study, we categorized the individuals on
the registry by the types of crime they
committed, their number of convictions, their
victims, and their areas of operation. By doing so,
we hope to better identify profiles of white-collar
offenders to suggest prevention and detection
strategies.
7. The study
We used Utah’s White-Collar Crime Offender Registry,1 which comprised 261 individuals convicted
between 2007 and 2018.2 However, the dataset
contained several measures that were deemed
irrelevant to the current study’s purpose: height,
weight, eye, and hair color. Variables retained in
the analyses include both offender and offense
characteristics. Table 1 presents descriptive statistics of these variables.
The registry also included 25 measures of whitecollar offenders’ targets/victims. We created a
series of dummy variables and regrouped them
into six distinct categories: industry, stakeholder,
personal, vulnerable, affinity, and random. The
registry also listed a total of 23 courts from eight
different district courts in which judgment was
rendered. We created seven dummy variables to
regroup these courts by the size of their county
populations. Refer to Table 1 for details of categories for target/victim and district courts.
Given the purpose of this study (i.e., the identification of several categories of white-collar offenders), we used the TwoStep cluster analysis as
our profiling method in SPSS version 25. The
Schwarz Bayesian Criterion was calculated to estimate the number of clusters, and a three-cluster
solution emerged from this analysis. We labeled
these clusters as the Tech Scammer, the Ponzi
Schemer, and the Insurance Fraudster. Additional
tests were conducted to identify significant
1
2
https://www.utfraud.com/Home/Registry
As of July 2019.
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Table 1.
Sample characteristics of Utah’s registered white-collar offenders (2007e2018)
Variables
N (%)
Age
Mean (SD)
Range
41.62 (12.31)
(18-80)
Gender
Female
66 (25.3%)
Multiple convictions
28 (10.7%)
Offense
Communications fraud
Insurance fraud
Money laundering
Pattern of unlawful activity
Securities fraud
Theft by deception
Breach of fiduciary duty
Mortgage fraud
114 (43.7%)
43 (16.5%)
17 (6.5%)
41 (15.7%)
46 (17.6%)
58 (22.2%)
15 (5.7%)
1 (.4%)
Target/Victim
Industry (e.g., healthcare providers)
Stakeholder
Personal
Affinity
Vulnerable
Random
115 (44.1%)
100 (38.3%)
42 (16.1%)
6 (2.3%)
23 (8.8%)
5 (1.9%)
County population size
Under 10,000
10,000-19,999
20,000-49,999
50,000-99,999
100,000-199,999
200,000-499,999
Over 500,000
6 (2.3%)
3 (1.1%)
14 (5.4%)
4 (1.5%)
31 (11.9%)
53 (20.3%)
156 (59.8%)
N Z 261
Note: The variable labeled as Target/Victim was dummy coded into 6 distinct categories of victim: (1) industry, which includes
small businesses and business owners, financial institutions, retailers, insurance companies, government agencies, healthcare
providers, landlords, and homebuyers; (2) stakeholder, which includes business partners, buyers/sellers, clients, employees,
employers, and investors; (3) personal, which includes family, friends/acquaintances, and single people; (4) vulnerable, which
includes persons with physical health issues, substance users, immigrants, the elderly, and minors; (5) affinity, which includes
members of religious, social, and military groups; and (6) random, which includes random individuals.
between-cluster differences. Table 2 presents
these cluster profiles, and we further illustrate
them in Section 7.
7.1. Tech Scammers
The first cluster, the Tech Scammer, comprised
35.6% of the subjects. On average, these individuals
are in their early forties (M Z 42.56 years old)
and more than 30% of them are female (M Z .35).
They tend to specialize in communications fraud
(M Z .68) but also engage in theft by deception (M Z
.32), patterns of unlawful activity (M Z .15), and
securities fraud (M Z .12). They mainly target
stakeholders (M Z .76) but also victimize persons
close to them (M Z .19) and industry members (M Z
11). In addition, they commit offenses in counties of
varying population sizes. We named this group “Tech
Scammers” as they seemingly use their knowledge
of technology to communicate with their victims at a
distance by mail, telephonic, or electronic means
with the intent to obtain property fraudulently.
7.2. Ponzi Schemers
The second cluster, the Ponzi Schemer, comprised
26.1% of the subjects. These individuals are
significantly older than those in the first group (M
Z 47.22 years old) and less likely to be female (M
Z .15). They engage primarily in securities fraud
(M Z .51) but also in patterns of unlawful activity
(M Z .29), money laundering (M Z 18), communications fraud (M Z .15), and breach of fiduciary
fraud (M Z .15). They mainly target stakeholders
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(M Z .4) but also victimize persons close to them
(M Z .28) and vulnerable people (M Z .28). Lastly,
they offend almost exclusively in counties with a
population over 500,0000 inhabitants (M Z .97).
We named this group “Ponzi Schemers” as they
echo the archetypical, middle-aged operator who
lures investors and pays profits to his earlier investors with funds from more recent investors.
7.3. Insurance Fraudsters
The third and largest cluster, the Insurance
Fraudster, comprised 38.3% of the subjects. These
individuals are among the youngest (M Z 36.93
years old). Akin to the Tech Scammers, they typically engage in communications fraud (M Z .41)
and theft by deception (M Z .26). Unlike subjects
in the other two cluster clusters, however, they
are often involved in insurance fraud (M Z .39). In
addition, all of them target industries such as insurance companies, namely healthcare providers.
Lastly, they tend to offend primarily in counties
with a population over 500,000 (M Z .6) but also
victimize individuals residing in counties with
populations ranging between 200,000 and 499,999
(M Z .18) and 100,000 and 199,999 (M Z .1) inhabitants. We named this group “Insurance
Fraudsters” because of their crime specialization.
8. Discussion
White-collar crime still remains a largely unexamined and complex social problem in business. By
offering a typology of white-collar offenders, organizations and communities can better identify
offender profiles, helping to prevent and detect
these criminals. In Section 8, we review the implications of the three specific groups in our typology.
8.1. Implications of prevention and
detection
First, Tech Scammers largely engaged in communications fraud, the most common form of whitecollar crime in our sample. Also, Tech Scammers
are the second largest group of offenders. Importantly, this group consists of a greater proportion
of women compared with the other two clusters.
This finding contributes to prior studies that found
women are more likely to engage in embezzlement, asset misappropriation, and identity theft
(Benson & Kerley, 2000; Weisburd et al., 1991,
2001). In general, Tech Scammers’ idiosyncrasies
show the usefulness of new internet-related
technologies to increase their crime perimeter in
less densely populated areas.
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Organizations must ensure that they are protected against cyberthreats (Chaudhry, 2017). In
2021, cybercrime cost US businesses more than
$6.9 billion. However, only 43% of businesses
currently felt financially prepared to face a cyberattack in 2022. According to these recent statistics, cybercriminals can penetrate 93% of company
networks (Brooks, 2022). It is essential for organizations to increase their security budgets and
adopt policies and procedures that protect themselves from sophisticated Tech Scammers.
While the internet and its far-reaching capabilities undoubtedly provide offenders with a
convenient weapon, from a policy perspective, it
can also be used to raise awareness among potential victims. For example, Holtfreter et al.
(2005) showed that sustained media coverage of
home and repair scams in Florida prepared local
citizens to detect and avoid them. In the same
vein, Michel et al. (2016) found that using the
internet as the predominant source of information
was associated with greater awareness of whitecollar crime.
Interestingly, Ponzi Schemers had the fewest
number of offenders in our sample, even though this
type of white-collar crime originally justified the
creation of the registry (i.e., Ponzi schemes).
Notwithstanding the deleterious impact of Ponzi
Schemers on their victims, they might not be as
common as previously assumed. Still, our results may
help potential victims avoid devastating financial
losses. The sociodemographic characteristics of this
particular group include variables traditionally
associated with respectability such as older age,
being male, and having business or financial occupations. Ponzi Schemers can easily use these factors
to quell suspicion among their victims. Potential investors should, therefore, be encouraged to watch
out for several red flags when considering partnerships with seemingly trustworthy individuals. Some
examples include high investment returns with little
or no risk, overly consistent returns, unregistered
investments, unlicensed sellers, secretive or complex strategies, issues with paperwork, and difficulty
receiving payments (U.S. Securities and Exchange
Commission, 2022).
Last but not least, our findings suggest that Insurance Fraudsters are the largest group of offenders. Insurance fraud is a very common form of
white-collar crimedand one that is typically and
almost exclusively committed by subjects in the
final cluster. These offenders were relatively
younger in age, averaging in their mid-thirties at
the time of their conviction and targeting industries including insurance companies, namely
healthcare providers. These findings are in line
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Table 2.
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Cluster profiles of Utah’s registered white-collar offenders (2007e2018)
Variables
Age
Female
Cluster 1
Tech Scammers
N Z 93
(35.6%)
Cluster 2
Ponzi Schemers
N Z 68
(26.1%)
Cluster 3
Insurance
Fraudsters
N Z 100
(38.3%)
Mean
Mean
Mean
42.56* (2, 3)
47.22* (1, 3)
36.93* (1, 2)
.35* (2)
.15* (1)
.23
Multiple convictions
.15
.04
.11
Communications fraud
.68* (2, 3)
.15* (1, 3)
.41* (1, 2)
Insurance fraud
.03* (3)
.01* (3)
.39* (1, 2)
Money laundering
0* (2)
.18* (1, 3)
.05* (2)
Pattern of unlawful activity
.15* (2)
.29* (1, 3)
.07* (2)
Securities fraud
.12* (2, 3)
.51* (1, 3)
0* (1, 2)
Theft by deception
.32* (2)
.03* (1, 3)
.26* (2)
Breach of fiduciary duty
.05* (2)
.15* (1, 3)
0* (2)
.01
0
0
Industry
.11* (3)
.07* (3)
1* (1,2)
Stakeholder
.76* (2, 3)
.4* (1, 3)
0* (1,2)
Personal
.19* (3)
.28* (3)
.05* (1,2)
Affinity
.01* (2)
.07* (1, 3)
0* (2)
Vulnerable
.04* (2)
.28* (1, 3)
0* (2)
Random
.03
.01
.01
Mortgage fraud
Under 10,000
.02
.01
.03
10,000-19,999
.01
.01
.01
20,000-49,999
.08
0
.07
50,000-99,999
.01
0
.03
100,000-199,999
.23* (2, 3)
0* (1)
.1* (1)
200,000-499,999
.38* (2, 3)
0* (1, 2)
.18* (1, 2)
Over 500,000
.32* (2, 3)
.97* (1, 3)
.6* (1, 2)
N Z 261
Note: The numbers in parentheses indicate clusters whose means are statistically different (*p < .05) based on ANOVAs with
Bonferroni post hoc tests.
with more recent data from several governmental
agencies, which suggest that insurance fraud is
one of the most costly and common forms of
white-collar
crime
(Federal
Bureau
of
Investigation, 2022; Louisiana Department of
Insurance, 2017).
The Federal Bureau of Investigation (2022) estimates the total cost of (nonhealth) insurance
fraud to be more than $40 billion per year. Common insurance fraud schemes include premium
diversion (i.e., embezzlement of insurance premiums), asset diversion (i.e., theft of insurance
company assets), and workers’ compensation
fraud. These fraud schemes not only impact the
companies’ finances but also their customers. As
such, fraud costs the average US family between
$400 and $700 per year in the form of increased
premiums (Federal Bureau of Investigation, 2022).
Additionally, insurance fraud can occur within an
organization by its internal stakeholders (e.g.,
employees, company officials, agents) or by
external stakeholders (e.g., policyholders or professionals who aid those making claims), further
contributing to its prevalence.
Our findings also suggest a shift in US criminal
practices toward healthcare providers. Prior
research indicates that health insurance fraud only
accounted for 6.5% of all healthcare expenditures 10
BUSINESS LAW & ETHICS CORNER
years ago (Anderson, 2012). Given health insurance
fraud is “alluring [and an] easy pile of cash” (Piper,
2013, p. 1), it is unsurprising that healthcare fraud
has become more prevalent. Some examples of the
most common health insurance fraud include billing
for services not rendered, billing a noncovered service as a covered service, and misrepresenting
dates, locations, or providers of service. Healthcare
fraudsters may include stakeholders inside and
outside the industry, such as patients, payers, employers, vendors, suppliers, and providers (e.g.,
pharmacists). In addition, organized crime rings and
computer hackers can also contribute to healthcare
fraud (Piper, 2013). Due to the complex nature of
these types of fraud, several stakeholders are at
risk, including the American public and the entire
healthcare delivery system. More recently, experts
also argue that the pandemic has further highlighted
the importance of identifying and reporting healthcare fraud (Jimenez, n.d.). As a result, organiz
