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. 2020 Jul 7;28(3):408–417. doi: 10.1080/13218719.2020.1780644

A comparison of fraud to fund gambling with fraud for other reasons

Erin Dougherty a, Lauren Staples b, Loyola McLean a, Toby Machart c, Bruce Westmore d, Olav Nielssen b,d,✉
PMCID: PMC9068002  PMID: 35530123

Abstract

In order to compare the characteristics, including diagnosed mental disorder, of people who commit fraud offences to fund gambling with those who committed fraud for other reasons, we examined a complete series of reports prepared for legal proceedings by two of the authors from between January 2002 and June 2019. A total of 160 fraud offenders were located, of whom 48 (30%) reported offending to fund gambling. Females made up 47.5% of the total sample and 43.8% of the gamblers. Of the problem gambling group, 44% had diagnoses of anxiety or depression, 44% had substance use disorder, 17% had bipolar and other psychosis, and 38% reported childhood trauma. There were no significant differences in the frequencies of diagnosis of mental illness or previous criminal convictions compared to the non-gambling group. Two thirds reported only gambling on poker machines. Only 19% of gamblers reported seeking treatment prior to being charged.

Keywords: addiction, fraud, gambling, gambling treatment, mood disorder, psychotic illness, substance use

Introduction

Australians have by far the highest gambling losses per capita in the world, at around $1250 per adult, which is 40% more than that of the next highest country, Singapore (The Economist, 2017). It has been estimated that 65% of Australians participate in some form of gambling-related activity annually (Warfield et al., 2016), and that around 2.5% of Australians are ‘at risk’ gamblers, and 0.8% of adults are ‘problem’ gamblers (Warfield et al., 2016; Williams, Volberg, & Stevens, 2012). Problem gambling has been linked to psychiatric disorder, suicide, relationship breakdown, substance use disorder, child neglect and abuse and a range of criminal behaviour (Lesieur & Rosenthal, 1991; Productivity Commission, 2010). It has been estimated that about 12% of homelessness in Australia is due to problem gambling (Machart et al., 2020). Because of the prevalence of problem gambling and the psychological consequences, problem gambling is a significant public health concern.

There are a number of studies examining the relationship between gambling disorder and other psychiatric conditions. A meta-analysis of co-morbid psychiatric disorders among treatment-seeking problem gamblers found high rates of current (74.8%) and lifetime (75.5%) co-morbid psychiatric disorders. This included high rates of current mood disorders (23%), alcohol use disorders (21%), anxiety disorders (18%) and non-alcohol substance use disorders (7%; Dowling et al., 2015). Studies of substance use in treatment-seeking problem gamblers have shown similar results with regard to substance use disorders, with 16% meeting the Diagnostic and Statistical Manual of Mental Disorders–Fourth Edition (DSM–IV) (American Psychiatric Association, 2000) criteria for alcohol abuse, 8% for alcohol dependence, 37.3% for nicotine dependence and 5.3% for cannabis abuse (Maccallum & Blaszczynski, 2002).

In the most recent revision of the Diagnostic and Statistical Manual of Mental Disorders (DSM–5) (American Psychiatric Association, 2013), Pathological Gambling was renamed Gambling Disorder and was moved from the Impulse Control Disorders Not Elsewhere Classified section to be included with Substance-Related and Addictive Disorders. A history of gambling-related illegal acts was removed from the diagnostic criteria in DSM–5, on the grounds that it did not improve diagnostic accuracy, as it was found to be present at lower rates than the other diagnostic criteria and was almost never present without multiple other criteria (Petry et al., 2014). Illegal conduct was removed from the diagnostic criteria for substance use disorder for similar reasons (Hasin et al., 2013). However, the prevalence of offending to obtain money for gambling among people with gambling disorder ranges from 14% to 30%, typically frauds or thefts associated with the need to obtain money to continue gambling or to cover losses (Granero et al., 2015). Hence the exclusion of the illegal acts from the diagnostic criteria for gambling disorder in DSM–5 is not a reason to ignore its clinical significance.

A recent systematic review noted the lack of a clear understanding of the risk factors for offending beyond financial stress among gamblers (Adolphe, Khatib, van Golde, Gainsbury, & Blaszczynski, 2019), and also noted that problem gamblers commit violent crimes at a greater than expected rate, although the authors noted that the nature of the association, and whether there was a causal relationship between gambling behaviour and violent and other offending, was also unclear (Adolphe, Khatib, et al., 2019). Gambling-related offending is reported to be associated with more severe gambling disorder, a higher rate of co-morbid mental illness and greater resistance to treatment (Ledgerwood, Weinstock, & Morasco, 2007). Risk factors for gambling-related offending are reported to include male gender (Momper, Delva, Grogan-Kaylor, Sanchez, & Volberg, 2010; Potenza, Steinberg, & McLaughlin, 2000; Potenza et al., 2001), younger age (Potenza et al., 2000) and greater financial difficulties (Momper et al., 2010). Higher severity scores on gambling diagnostic questionnaires (South Oaks Gambling Screen, DSM–5 for gambling disorder and Addiction Severity Index–Gambling), have been found to be associated with gambling-related illegal activity in the three studies that we were able to locate that reported these measures (Gorsane et al., 2017; Ledgerwood et al., 2007; Mestre-Bach et al., 2018b), demonstrating a strong relationship between gambling-related offending and the most severe form of gambling disorder. Higher scores for antisocial personality disorder (Gorsane et al., 2017; Ledgerwood et al., 2007) and impulsivity (Ellis et al., 2018; Mestre-Bach et al., 2018a) have also been found to be associated with gambling-related illegal activity. However, we were unable to locate any studies comparing the characteristics of gambling-related fraud offenders to those who committed fraud offences for other reasons.

The aim of this study is to compare the demographic, clinical and offending of people charged with fraud offences who reported committing the offences to fund gambling with those of people who committed fraud offences for other reasons.

Method

Data were extracted from a complete series of reports prepared for sentencing proceedings in fraud-related matters by two psychiatrists (O.N. and B.W.) between January 2002 and March 2019. The clients were referred for psychiatric assessment for courts, rather than for treatment of gambling or other disorder. The report databases of the participating psychiatrists, with a combined total of around 10,000 individual reports in criminal matters, were searched for the terms ‘fraud’, ‘obtain financial benefit by deception’, ‘larceny’ and ‘larceny as a servant’.

The following data were extracted by E.D.:

  1. Demographic information, including age, sex, marital status, occupation and past criminal convictions (excluding drink driving and minor traffic matters).

  2. Clinical details, including primary psychiatric diagnoses, comorbid disorders and substance use disorder.

  3. Details of the offences, including the motivation, method, duration, amount of money involved, offence details, diagnoses of mental illness and pattern of substance use.

  4. Among those who reported committing offences to fund gambling, the pattern of gambling behaviour.

Anonymised data were recorded on a spreadsheet and analysed using frequency tables and χ2 and t test statistics, comparing the characteristics of those who disclosed committing fraud offences to fund gambling with the characteristics of those who committed fraud offences for other reasons. A further analysis compared fraud offending and gambling according to sex.

Results

Demographic, clinical and offence characteristics

Reports for a total of 160 fraud offenders were located and included in the study, of whom 48 reported being motivated by the need to obtain money to fund gambling. The remaining 112 cases made up the comparison group. The mean age of the sample was around 40, with gamblers non-significantly older than non-gamblers. Nearly half the sample (76/160, 47.5%) were female, who made up a similar proportion of the gamblers (21/48, 43.8%). A higher proportion of gamblers were in occupations in which they had direct financial control or where they were trusted with financial decision making (42% vs. 23%, p = .01), whereas more of the non-gamblers were unemployed at the time of the offences (38% vs. 17%, p = .01).

Most of both groups committed multiple offences over long periods, with the mean duration of offending non-significantly higher for gamblers (2.7 years vs 1.9 years, p = .07). There was a large range in the amounts stolen by both groups, with the mean slightly higher for gamblers, and the median amount higher among the non-gamblers. With regards past criminal convictions, there was no difference between the gambling group and those who committed fraud for other reasons (39% vs. 35%, p = .64). However, there was an unexpectedly higher rate of previous criminal convictions among the female offenders (49% vs. 29%, p = .01).

Nearly half of both groups were found to have anxiety or depression, although because of the retrospective nature of the assessments it was not possible to reliably establish whether they were primary mood disorders or secondary to circumstances associated with the offences and subsequent prosecution. Nearly half of both groups were also found to have a comorbid substance use disorder (46% and 44%, respectively). A higher proportion of the non-gamblers were diagnosed with a psychotic disorder, most commonly bipolar disorder, but the difference did not reach significance (28% vs. 17%, p = .16; Table 1).

Table 1.

Demographic, clinical and offence characteristics of fraud offenders with and without gambling disorder.

  No gambling disorder (N = 112)
 Gambling disorder (N = 48)
 Test for difference
  
Fraud offenders (N = 160) M (SD) N (%) Range Median M (SD) N (%) Range Median χ2 t p
Age 39.0 (11.7)       42.1 (11.8)         1.50 .14
Males   57 (51)       27 (54)     0.14   .73
Duration of offending (days) 703.6       987.1         1.47 .07
Quantum (Australian dollars) 550,612   900– 16,000,000 100,500 704,008   40– 10,000,000 62,000   0.40 .69
Multiple offences   95 (85)       40 (83)     0.06   .82
Anxiety and depression   51 (46)       21 (44)     0.04   .86
Bipolar and other psychosis   31 (28)       8 (17)     2.21   .16
Substance use disorder   52 (46)       21 (44)     0.09   .99
Childhood trauma   50 (45)       18 (38)     0.17   .73
Financial occupationa   23 (21)     20 (42)       7.63   .01
Unemployed 42 (38)       8 (17)       6.79   .01
Previous criminal convictions 44 (39)       17 (35)       0.21   .64
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aIncluding positions of financial authority and trust such as manager, lawyer and estate agent.

Gambling characteristics

Poker machine gambling was by far the most common (67% of the sample), followed by horse racing and more recently sports betting by young males. However, sports betting was associated with by far the largest thefts, with a mean of more than two million dollars. Other gambling included gambling at casinos and in private card games, and also in multiple formats, and was associated with the longest duration of gambling, of more than 3.3 years, followed by fraud to fund poker machine gambling of an average of 2.5 years before being charged. Most reported attempts to stop gambling, but only a fifth (9/48, 19%) of the offenders reported seeking professional treatment for gambling prior to being charged (Table 2).

Table 2.

Gambling characteristics.

Gamblers (n = 48) N (%) Males (%) Quantum (Australian dollars) M Duration (days) M (SD)
Poker machines 32 (67) 18 (56) 488,692 919.3 (992.6)
Horse racing 13 (27) 8 (62) 1,201,718 754.8 (88.1)
Sports 5 (10) 4 (80) 2,050,600 594.0 (621.2)
Other 9 (19) 3 (33) 374,711 939.9 (1212.6)
Prior engagement in treatment 9 (19) 5 (56)    
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Gender differences

Nearly half the sample were female, and the proportion of female fraud offenders who were gamblers and the mean duration of offending were similar to those of males. Females were more likely to be poker machine gamblers, and to be employed in positions of trust, particularly in financial controlling positions, notwithstanding the higher proportion with previous criminal convictions. The striking difference between male and female offenders was in the quantum of money taken, with females stealing a mean of more than a million dollars, compared to a mean of around $250,000 for male fraud offenders (Table 3).

Table 3.

Fraud offences, gambling and gender.

  Males
(n = 84)
 Females
(n = 76)
 Test for difference
  
Fraud offenders (N = 160) M (SD) N (%) M (SD) N (%) t χ2 p
Age 40.6 (10.9)   39.2 (12.7)   0.79   .43
Gambling disorder   27 (32)   21 (28)   39 .53
Previous criminal conviction   24 (29)   37 (49)   6.84 .01
Duration of offending (days) 837.7 (991.5)   737.4 (903.5)   0.56   .58
Quantum (Australian dollars) 253,532   1,083,072   2.18   .03
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Discussion

This study presents for the first time a comparison of the characteristics of people with problem gambling in a large sample of fraud offenders. The study confirms that unlike almost every other category of offending, the number of female fraud offenders are similar to those of males. The study also confirms the high rates of comorbid psychiatric disorder among fraud offenders as a group, in particular, mood disorders and substance use disorders. Mood disorders appear as a cause and a consequence of both offending and gambling behaviour, and because of the retrospective nature of the assessments and the circumstances in which the assessments were conducted, it was often not possible to establish whether mood disorder preceded gambling behaviour and contributed to gambling. However, poker machine gamblers in particular often reported gambling as a way of obtaining temporary relief from distressing mood states. The high rate of comorbid substance use disorder in both groups confirmed the overlap between substance use and gambling. Among gamblers, disinhibiting drugs, particularly alcohol, and drugs affecting similar reward pathways, such as stimulant drugs, were often cited as exacerbating gambling behaviour. Among non-gamblers, fraud offences were more likely to be committed to pay for drugs of addiction.

A higher proportion of gamblers were in positions of financial authority, including accounts clerks, financial controllers, and also managers and operators of trust accounts such as lawyers and estate agents, which suggests that it is not only the severity of problem gambling but the opportunity to commit offences that determines whether gamblers will commit fraud offences. The non-gamblers were more likely to be unemployed, which is a not surprising, as a number of the offences involved social security fraud. There were also offences committed by professional criminals, such as members of criminal groups, often from overseas, involved in credit card offences. However, there were no differences between the two groups in the proportion who had previous criminal convictions, and the only surprising finding was the higher proportion of previous convictions among the female offenders in both groups.

A disproportionate number of both groups met the accepted criteria for the diagnosis of psychotic illness, in particular bipolar disorder. A feature of the manic phase of bipolar disorder is increased pleasure-seeking behaviour and glib rationalisations about the potential consequences of financial transgressions. There were also cases of increased confidence about the outcome of gambling and even magical thinking associated with the outcome, also associated with manic episodes. The results are consistent with the observation that people in manic states are more likely to commit property offences than serious violent offences (Nielssen, Malhi, & Large, 2012). However, people with mental illness may be over-represented in this sample through the process of referral.

An interesting feature of the sample is the number of capable and otherwise rational people who became addicted to poker machine gambling, which is well known to have fixed unfavourable odds, and then funded ongoing poker machine gambling by committing frauds with a high likelihood of detection. A frequent explanation given by the subjects of this series was the way poker machine gambling allowed the player to detach from other problems, including the financial difficulties created by gambling, similar to the ‘escape mechanism’ provided by gambling described elsewhere (Schull, 2002). Poker machine gamblers have also been described as entering an altered state of awareness in which control over the duration and amount gambled is diminished (McKeith, Rock, & Clark, 2017). A number of reports noted the effect of visual and auditory cues of poker machines on the urge to gamble and gambling to relieve stress (McKeith et al., 2017). The mechanism of addiction to poker machines may be quite different to the intense engagement seen in gamblers on horse races and sporting events, who often report around-the-clock attention to news that might influence the outcome of events. For sports gamblers, the excitement appears to be during the event, when a significant amount hangs on the outcome, rather than the result, because large winnings did not relieve the urge to gamble.

The findings of this study add to the ambiguity about the status of gambling disorder in sentencing for criminal matters, and also the role of treatment directives in disposals from the courts (Adolphe, van Golde, & Blaszczynski, 2019). Gambling disorder is classified as a non-substance-related addictive disorder in DSM–5 (Petry et al., 2014); this classification is not without controversy, and there has been some debate as to whether it should be considered an impulse control disorder or an addictive disorder (Blanco, Moreyra, Nunes, Sáiz-Ruiz, & Ibáñez, 2001). Support for gambling as an impulse control disorder comes mainly from neurobiological studies that found similar changes in the serotonin metabolites and other markers in patients with impulse control disorders and gambling disorder (Blanco et al., 2001; DeCaria, Hollander, & Grossman, 1996). Evidence for gambling as a disorder of addiction includes the similarities in behaviour with the substance-related addictive disorders including the development of a tolerance to the excitement caused by gambling, withdrawal symptoms, anticipatory cravings and a chronic relapsing course (Blanco et al., 2001; Petry et al., 2014). Dysregulation of dopaminergic pathways may also play a role in the development of gambling disorder, which is supported by the many reports of patients with Parkinson’s disease developing problem gambling after commencing dopamimetic medications (Dodd et al., 2005). Regardless of the aetiology of gambling disorder, the salience of gambling behaviour over self-interest in many cases and the widespread community-sanctioned availability of opportunities to gamble support the argument for gambling addiction to be a mitigating factor in sentencing.

The prevalence of problem gambling among fraud offenders in this study raises the potential of diversion of some gambling offenders for treatment, including through therapeutic courts such as the gambling court in South Australia (Adolphe, van Golde, & Blaszczynski, 2019). Meta-analysis results from psychological therapies for gambling disorder suggest that cognitive behavioural therapy (CBT) is effective in reducing gambling behaviour and symptoms of gambling disorder in the immediate aftermath of treatment. How long those gains last has not been established, and relapse is common. There is preliminary evidence that motivational interviewing reduces gambling behaviour, but not the other symptoms of gambling disorder, although this was based on the findings from a limited number of studies (Cowlishaw, Dowling, & Anderson, 2012). There is some evidence coming from small trials that the use of selective serotonin reuptake inhibitors (SSRIs) reduces gambling behaviours independent of their effect on mood and anxiety disorders (Hollander et al., 2000; Saiz-Ruiz et al., 2005). There is also evidence from two small randomised control trials that the opioid receptor antagonist naltrexone results in improvement in gambling severity scales (Grant, Kim, & Hartman, 2008; Kim, Grant, Adson, & Shin, 2001). However, there are currently no medications with a specific indication for the treatment of gambling disorder approved for use in Australia, and the main form of widely available treatment is group therapy using the 12-step model of Gamblers Anonymous.

This study has a number of significant limitations. The sample is drawn from consecutive referrals to two psychiatrists, and as such does not represent a complete sample of fraud offenders, and would be expected to include a greater proportion of offenders with pre-existing psychiatric disorder. However, courts increasingly rely on mental health professionals to both elicit the subjective circumstances and explain offending behaviour, and for guidance regarding treatment and prognosis. For example, the files of more than half of the offenders charged with serious violent offences in the District Court included a report by a mental health professional, many of which did not include the diagnosis of a major psychiatric disorder (Yee, Large, Kemp, & Nielssen, 2011). Another limitation is that it only includes people who have been charged with offences, and does not include matters that were not detected, or were dealt with by mediation or civil remedies, or even by no action. However, the nature of the sample is also a strength, as it includes expert diagnosis and detailed qualitative information about the reasons for offending. Another limitation is the absence of any systematic data on gambling behaviour, other than the self-reported gambling formats. A further limitation is the gambling behaviour of the fraud offenders who did not report being motivated by gambling losses was not recorded, despite the high prevalence of other forms of addiction in this group and the widespread nature of gambling behaviour in the population (Productivity Commission, 2010).

The opportunities to gamble in Australia continue to expand. The advent of online gambling in the last 20 years has made it possible for the first time for people to gamble in complete isolation. The wide availability of poker machines in Australia, especially in New South Wales, which in 2014 had more than 95,000 poker machines, second only to Nevada, means that it has never been easier to gamble (Ziolkowski, 2014). The regulation of gambling has become a difficult issue, now that state and territory governments are so reliant on gambling revenue, which in New South Wales is in the order of 3% of total revenue annually (Treasury, 2017). This is despite the very regressive nature of gambling taxes, and the many social costs of problem gambling, which are largely met by services funded by state governments – mental health services, criminal courts and prisons. Hence in addition to the possibility that gambling addiction should be considered a mitigating factor in some cases, perhaps some responsibility should be taken by the community at large for enabling addictive forms of gambling.

Conclusion

There was a high rate of mental disorder among fraud offenders, including those who reported committing fraud offences to fund gambling addiction. Given the high prevalence of gambling in the community, the harms caused by problem gambling, the low rate of care-seeking behaviour among offenders and limited availability of effective treatments, greater attention should be made to a history of gambling behaviour in primary care. The findings have implications for screening people in positions of trust and fraud prevention strategies. The salience of gambling behaviour in this group adds to the debate regarding whether gambling addiction should be considered a mitigating factor on sentencing and whether diversion to treatment was indicated in some cases.

Ethical standards

Declaration of conflicts of interest

Erin Dougherty has declared no conflicts of interest

Lauren Staples has declared no conflicts of interest

Loyola McLean has declared no conflicts of interest

Toby Machart has declared no conflicts of interest

Bruce Westmore has declared no conflict of interest

Olav Nielssen has declared no conflicts of interest

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the University of Sydney Human Research and Ethics Committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.

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