Canadian Journal of Communication Vol 44 (2019) 331–342  ©2019 Canadian Journal of Communication Corporation

Welfare Fraud 2.0? Using Big Data to Surveil, Stigmatize, and Criminalize the Poor

Kathy Dobson, Carleton University

Kathy Dobson is a PhD candidate in the School of Journalism and Communication in the Communication Department at Carleton University. Email: .


Background This article examines the discourse around the digital surveillance of those living on social assistance by analyzing two digital “anti-fraud” tracking tools: Ontario’s Social Assistance Management System and Australia’s BasicsCard system.

Analysis  Digital surveillance and big data analytical processes embedded in the utilization of digital “anti-fraud” tracking tools tend to operate outside of democratic processes, outpace ethical considerations, and even create and reinforce social divisions and inequality.

Conclusion and implications  Despite the claim that these software programs make about saving taxpayers’ money and assisting those in need more effectively, these digital tools adversely affect the people they are supposed to help and, worse, stigmatize and criminalize those who live in poverty.

Keywords Big data; Surveillance; BasicsCard; Social Assistance Management System (SAMS); Welfare; Anti-fraud software


Contexte  Cet article examine le discours entourant la surveillance numérique des assistés sociaux en analysant deux outils prétendument antifraudes : le Système automatisé de gestion de l’aide sociale en Ontario et le système BasicsCard en Australie.

Analyse  L’utilisation d’outils numériques « antifraudes » entraîne une surveillance numéri-que et des processus d’analyse de mégadonnées qui tendent à entraver les processus démocratiques et les considérations éthiques, jusqu’à créer et renforcer des divisions et des inégalités sociales.

Conclusion et implications  On prétendra que ces logiciels allègent le fardeau fiscal des contribuables et permettent d’offrir une aide plus efficace à ceux et celles qui en ont réellement besoin. En réalité, ces outils numériques ont un effet néfaste sur ceux et celles qu’ils sont censés aider et, pire, ils stigmatisent et criminalisent les pauvres.

Mots clés  Mégadonnées; Surveillance; BasicsCard; Système automatisé de gestion de l’aide sociale; Aide sociale; Logiciel antifraude


Big data, with its promise of “complete” data sets, comes with an enormous level of complexity, both in terms of storage and data analysis (Harford, 2014). Those data sets, however, also come with important concerns and questions about ethical issues and social implications in big data research, in particular, when considering marginalized communities such as those who live in poverty (Crawford, Miltner, & Gray, 2014; Shilton, 2012). Traditional methods for tracking welfare fraud are being supplemented with new tools and technological platforms, drawing on informal data streams and large databases to detect possible instances of fraud and take away social assistance from flagged individuals (Dandeker, 1990; Dee, 2013; Little, 2001). This is despite the fact that welfare fraud is rare. In Canada, for example, it is estimated at less than one percent of welfare recipients annually (Maki, 2011; Mirchandani & Chan, 2007; Mosher & Hermer, 2005), and similarly low rates are found in the United States, Australia, and the U.K. (DWP, 2012; NAO, 2008; Prenzler, 2011). Yet governments in each of these countries are becoming more aggressive and punitive of welfare “cheats” (Chann, 2012; Gavigan & Chunn, 2006; Gustafson, 2009; 2011; Marechal, 2015; Marston, 2008; Marston & Walsh, 2008; Varma & Ward, 2014).

In Ontario, Canada, for instance, the Social Assistance Management System (SAMS) [Ontario Ministry of Children, Community and Social Services, 2015]recently replaced the Consolidated Verification Process (CVP) [Ontario Ministry of Children, Community and Social Services, 2015], a province-wide database that used certain “risk factors” to identify people living on social assistance as potential frauds (Herd & Mitchell, 2003). The new digital anti-fraud software program, SAMS, serves to individualize the “problem” of poverty through the spectre of fraud and uses an ever-widening net of surveillance practices, sharing collected data across multiple government platforms. Similar databases and surveillance tools are being used in the United States, England, Australia, and other countries (Dee, 2013; Dickinson, 2013; Fletcher & Wright, 2018; Humpage, 2016; NAO, 2008).

This article examines these digital “fraud” tracking tools, drawing on the surveillance assemblage theory to chart out how these systems increasingly have the power to draw on discrete flows of information to detect, monitor, and punish a marginalized group of people—those living in poverty—as part of a larger neoliberal strategy of reducing social assistance (Eubanks, 2006; Haggerty & Ericson, 2000; Marston, 2008; Piven & Cloward, 1971; Rodger, 2012). Surveillance assemblage theory is particularly useful here, as surveillance “converges” through people’s use of different platforms. Information is gathered and people are essentially turned into information “flows.”

Since marginalized communities often lack power and political clout, they are often designated as the “testing grounds” for various surveillance technologies, including repressive social programs (Eubanks, 2014). The increased surveillance of those receiving social assistance is justified in order to force welfare recipients off welfare and into the work force (Berger, 2001; Eubanks, 2014; Maki, 2011). When the state utilizes these anti-fraud software programs through partnerships with private high-tech companies and apply data analytics to individuals based on algorithms and predicting their future behaviour, it is holding people accountable and punishing them in advance of actions they have yet to do. This means some of the most vulnerable people are often victims of these new digital anti-fraud software programs, especially those programs that are often tested in real-time and unleashed before they are ready. 

Case study and methodology

The purpose of this article is to examine the discourse around the digital surveillance of those living on social assistance. It analyzes two digital “anti-fraud” tracking tools: Ontario’s Social Assistance Management System (SAMS) and the BasicsCard system in Australia [Ontario Ministry of Children, Community and Social Services, 2015; Dee, 2013]. Employing the methodology of critical discourse analysis (Fairclough, 1992; 1995; 2003), this includes an examination of select government policy papers and reports, mainstream media news articles, and private industry websites (specifically those high-technology companies awarded government contracts to create and implement the software programs) and public releases. This article also draws on a case-study approach that allows for an in-depth, multifaceted examination of complex situations and issues in the context of real-life settings. Through the combination of an analysis of news media coverage, especially public reception and user reactions, industry and governmental reports and policy papers, public announcements and releases, and government evaluations and reports on the programs, this case-study analysis attempts to approach the deployment, use, and impact of these welfare fraud-detection systems in a holistic manner. 

The Ontario Social Assistance Management System (SAMS)

Developed by the private high-technology company Cúram, the Social Assistance Management Systems (SAMS), which took four years to develop and implement, is a provincial computer system intended to manage welfare payments more efficiently and cut down on user fraud, thereby saving tax dollars. First introduced in Ontario in November 2014, this new anti-fraud software was created by private industry through a partnership with the Ontario government at an initial estimated approved budget of $202.3 million. It was intended to deliver benefits to those covered by Ontario Works and the Ontario Disability Support Program and Assistance for Children with Severe Disabilities (Office of the Auditor General of Ontario, 2015). Despite the stated goals of the program to cut back on ineligible applicants, reduce costs, uncover fraud, and improve client services, it failed to do so; SAMS experienced numerous “glitches,” including incorrectly issued social assistance cheques, eligible recipients removed from the system, and a failure to maintain correct financial records (Office of the Auditor General of Ontario, 2015). Further, cost overruns meant that the initial budget of $202.3 million ballooned to more than $300 million as of 2016, with millions more over budget each year since its initial launch (Jones, 2016). Beyond the challenges and cost overruns and initial computer “glitches,” however, is the fact that SAMS users are being surveilled and tracked across an assembly of platforms and services (Ministry of Community and Social Services, 2015).

As David Herd and Andrew Mitchell (2013) posit, “New technologies and administrative practices are being deployed to achieve the provincial goal of restricting access to income support in a system of ongoing surveillance, completely at variance with the stated objective of assisting people to achieve independence” (p. 119). This suggests that the predicted tax savings come at the expense of the very people this new digital software program was supposed to help most. More complex eligibility requirements also mean that eligible applicants are failing to access services or are inappropriately removed from them (Office of the Auditor General of Ontario, 2015).

In this case, the practice of surveillance is unequal since it creates a power dynamic between the watcher and the watched, particularly in the case of those living in poverty—people often singled out for more aggressive scrutiny (Eubanks, 2014; Gilliom, 2001; Henman, 2005; Lyon, 2008; Richards, 2013). Surveillance in welfare has expanded the state’s power to regulate, monitor, and control people in poverty (Chamlin, Burek, & Cochran, 2007; Dee, 2013; Dodenhoff, 1998; Dornan & Hudson, 2003; Fitzpatrick, 2005; Fuchs, 2012; Monahan, 2008; 2010; Stoddart, 2014).

“The modern economy and the modern state depend on the control of workers, consumers … and citizens. Surveillance is a form of domination that is an inherent feature of capitalism” (Fuchs, 2015, p. 6). This includes surveillance as a tool for the intimidation, stigmatization, and humiliation of the poor (Murray, 2000; Pollack, Danziger, Seefeldt, & Jayakody, 2002), as demonstrated by mandatory urine tests, finger prints, surprise home visits, and even the specific language of welfare support (Budd, 2011; Henman, 2004; 2005; Lyon, 2008; Murray, 2000; Zureik & Hindle, 2004). For those who do apply for assistance, in addition to the numerous barriers they must first overcome, there is ongoing, “punishment for their plight,” and they will have to deal with the moral regulations surrounding those living in poverty and the welfare reform set up to discipline the poor and those “at risk” of poverty(Gilliom, 2001, p. 40). In other words, welfare assistance has been replaced by “workfare” as a way of controlling those living in poverty (Maki, 2011; Molander & Torsvik, 2015; Wacquant, 2009). 

BasicsCard in Australia

These often repressive and humiliating forms of the surveillance of welfare recipients now include, for example, income “management,” which is the basis of the BasicsCard in Australia (Knaus & Davey, 2017). This is a cashless social-assistance payment system that involves using a card that limits recipients to only purchase pre-approved items. In addition to being able to track, surveil, and collect massive amounts of personal information, as with SAMS in Ontario, this program also suggests an inherent lack of trust in those living in poverty, revealing just one of the ways the state creates “deviants” out of those who are not “good” consumers in a capitalist society, since spending money is the “main means of social integration and participation in a consumer democracy” (Campbell, 2004, p. 89). The BasicsCard in Australia, first introduced in the late 2000s and initially intended to target Indigenous communities, “quarantines” 80 percent of welfare payments on the card so it cannot, for example, be used to withdraw cash, purchase alcohol, or buy goods at any retail store not pre-approved by the state. The card’s use is limited not only to specific items but also to government-approved vendors. This digital “welfare” card reduces the control that social-assistance recipients have over this income, and many of the users have spoken out about the lack of autonomy with the BasicsCard (Humpage, 2016). For example, some benefit recipients have expressed concern about not being able to provide their children with cash to pay for field trips or school supplies or any other cash-based school necessities, and they describe the card as humiliating to use (Australian Government Department of Social Services Final Report, 2017; Davey, 2017; Kennedy, 2012). By limiting the ability of card users to make choices about their purchases, the BasicsCard demonstrates a lack of trust and confidence in the user and many recipients have complained of the stigma they feel when using the card (Davey, 2017; Murphy-Oates, 2016).

Further, the application of BasicsCard also demonstrates a disregard of users’ privacy and, consequently, put users—who are socio-economically marginalized to begin with—in a more vulnerable position. The private Australian company that created and administers the card, Indue (2018), states under the card’s terms and conditions that it can collect and store personal information, such as the age, gender, address, welfare payments, and transactions attached to each account, including deposits and withdrawals. The company can also share and report on this data mining with other agencies, including, but not limited to, the social service department of the Australian government (see Indue, 2018).1

Impact, representation, and framing

A significant amount of research has examined the representations of those living in poverty in the mainstream media, including newspapers and television news reports. Researchers have noted a number of patterns in the ways in which the “poor” are framed and suggest these frames have important and strong impacts on the public’s perception and understanding of what poverty means (Cunningham, 2004; El-Burki, 2013; Fleras, 2011; Kensicki, 2004; Lister, 2004). For example, a study by Max Rose and Frank R. Baumgartner (2013) of media coverage and the framing of the poor between 1960 and 2008 in the United States identified five distinct frames. These frames include laziness and avoidance of work, cheating the welfare system, and the criminalization of the poor. Rose and Baumgartner’s (2013) study showed that media discussions of poverty have moved from arguments that focus on the structural causes of poverty to portrayals of the poor as welfare cheats whose support from government programs does more harm than good, and that policy has followed the framing. “Our data suggest that this focus on the individual, as opposed to the system, maybe be one of the most important elements of the general ideological ascendance of neoliberalism in American politics since the 1970s” (Rose & Baumgartner, 2013, p. 43). The literature on poverty representation in the news media often argues that the media act as a key conduit of information, play a major role in forming collective public opinion, and act as a primary definer of social issues. As such, what the public believes about those living in poverty is assumed in much of the literature to often be formed and reinforced by mainstream media dialogue and the presentation of those living in poverty (Cunningham, 2004; El-Burki, 2013; Fleras, 2011; Kim & Loury, 2014; Lens, 2013; Lister, 2004; Mosse, 2010; Redden, 2009; 2011; Swanson, 2001).

The literature analyzing the press and media coverage presents journalists and the mainstream news media as an important source of confirming and perpetuating negative stereotypes of those living in poverty and, as a result, further stigmatizing them (Lister, 2004). Therefore, the mainstream media continues to be viewed as an important influence on how the public understands these issues, which presumably includes public perceptions of the necessity of anti-fraud software to track those receiving social assistance. The analysis of media framing in television images and the print media also makes it clear that those living in poverty are not always just presented as negative stereotypes of the “lazy welfare cheat” but, in fact, are often also depicted in a sympathetic or even neutral tone (Bullock, Wyche, & Williams, 2001; Rose & Baumgartner, 2013). These depictions and framing of the poor, however sympathetic or neutral they intend to be, still typically lack the complexity necessary to educate the public about the root causes of poverty, as they fail to contextualize poverty or offer insight into the systemic causes (Bullock et al., 2001).

Much of the literature argues that the media have the potential to inform and shape public opinion by offering counter narratives to the pervasive negative stereotypes about those receiving public assistance as being lazy and unworthy (Jackson, 1997; McLaughlin, 1997; Wilcox, Robbennolt, O’Keeffe, & Pynchon, 1996). Therefore, consideration needs to be given to the question how the impact of the media’s coverage of the BasicsCard system in Australia and SAMS in Ontario influences the public’s perception of those who live in poverty, and the consequent rationale for software that is intended to surveil, sort people into specific categories, and share data across multiple platforms.


There is increasing concern and recognition of how digital surveillance and big data analytical processes to collect data may operate outside of democratic processes, as the technology is developing and evolving so rapidly that it is outpacing ethical considerations, while its inherent targeting often creates and reinforces social divisions and inequality (Henman, 2004; Stoycheff, 2016). As David Lyon (2011) and others have posited, profiling and prediction policy can lead the state to make troubling decisions. The state makes assumptions based on data collected through surveillance and then groups people according to their potential behaviours (Lyon, 2008; Stoycheff, 2016). For those assigned to a group that predicts criminal or other antisocial behaviour, including the “crime” of living in poverty or not proving economic viability, the consequences can be enormous and potentially devastating, as one is determined to be outside the group of citizens most valued and empowered by the state. This raises critical questions in regard to fairness and equity (Budd, 2011; Lyon, 2003; 2011; Monahan, 2016; Stoycheff, 2016).

For example, predictive algorithms that draw on accumulated personal digital data are used to determine which individuals should have access to certain goods and which individuals should be placed in a “risky” category (Crawford & Schultz, 2014). One of the associated concerns includes what Rob Kitchin and Tracey Lauriault (2014) posit, which is that “data infrastructures are never neutral, essential, objective: their data never raw but always cooked to some recipe by chefs embedded within institutions that have certain aspirations and goals and operate within wider frameworks” (p. 8). Kitchin and Lauriault (2014) argue that part of the job of predictive profiling is “anticipatory governance,” and the concern here is that predictive analytics are used to assess and decide appropriate responses and actions even though “a person’s data shadow does more than follow them; it precedes them, seeking to police behaviours that may never occur” (p. 12). As Bernard Harcourt (2006) posits, using predictive analytics as a form of anticipatory governance often results in punishing individuals based on assumptions drawn from dataveillance systems as opposed to actual behaviours.

Myths around welfare fraud are used to justify surveillance systems that, in turn, act to reframe social problems “in relation to market forces … to force recipients into low-paying, precarious labour” (Maki, 2011, p. 60). Misconceptions and negative stereotypes about “the poor” also mean a lack of public support or increased pressure for timely action from state officials when it comes to addressing problems. This occurred, for example, in the United States when some states switched over to a computer program that meant thousands of welfare recipients, including children, had to go without food (Smith, 2017). This computer “glitch” was eventually resolved, but only after thousands of families going without their welfare checks, leaving many of them without food or money to pay their rent.

What impact and effect do these technologies have on those who are living in poverty? Brian Waddell (2001) suggests that, “the Welfare state … is a target because it grants citizens economic rights and has made the state system an arena in which market imperatives and capitalist prerogatives can be challenged” (p. 133). If surveillance technologies are reproducing pre-existing inequalities, “situating the poor in a broader network of crime and criminalization” (Hier & Greenberg, 2009, p. 11), what other culturally ingrained values are reflected in data mining and surveillance? Further, even if there are laws that intend to protect the public from invasive and excessive surveillance and big data collection practices, how can government and commercial (private industry) surveillance programs be critically analyzed or challenged if they are secret?  


Although this research is ongoing as part of a larger research project, an examination of these social service software systems in two different countries did reveal some commonalities and numerous troubling facts. Both of these digital software programs are initially pitched and sold to the public in the mainstream media as a way to save taxpayers’ money, as an efficient tool for catching welfare “cheats” and exposing welfare “fraud.” This makes them highly appealing and invaluable, as it reflects and reinforces some of the most popular negative misconceptions and stereotypes about the “poor,” including the claims that, “anyone can pull themselves out of poverty: The Boot straps Myth” and “those who are in poverty are lazy, ‘Welfare queens,’ and/or irresponsible: the individual faults myth” (Ullucci & Howard, 2015, p. 175). Further, the software companies that design and develop these anti-fraud software programs also claim they will be able to serve the targeted client more. Yet these digital high-tech programs often contribute to the further marginalization and humiliation of people in poverty—the same people who are among the most vulnerable (Australian Government Department of Social Services, 2017; Davey, 2017. Despite the claims that these programs make about saving taxpayers’ money and assisting those in need more effectively, both the BasicsCard and SAMS experienced significant cost overruns and other difficulties,2 often referred to and dismissed as simple “glitches” around their initial launch. In Ontario, SAMS continues to experience numerous “glitches,” and this means delays and significant challenges for the very clientele it is intended to help (Office of the Auditor General of Ontario, 2015; 2017). These digital tools not only adversely affect the people they are supposed to help, these same people often end up being flagged as welfare “cheats,” which makes it much more difficult for them to apply for social assistance (Chann, 2012; Dee, 2013; Gavigan & Chunn, 2006; Gustafson, 2009; 2011; Marston & Walsh, 2008; Monahan, 2008; Mosher & Hermer, 2005). These programs also increase wait times for those who apply for assistance and make it more challenging to apply (Ministry of Community and Social Services, 2015). Finally, many of professed goals and promised outcomes of the software programs have yet to be achieved (Ministry of Community and Social Services, 2017). They cost more than budgeted for, fail to save taxpayers’ money through the promised goal of uncovering welfare “cheats,” and, to date, have yet to increase efficiencies and the delivery of services to those who need them most (Kennedy, 2012; Knaus & Davey, 2017; Office of the Auditor General of Ontario, 2015). Perhaps most troubling of all, however, is the uncontested practice and degree of surveillance and social sorting resulting from the data mining, which is being used to not only surveil but also stigmatize and criminalize those who live in poverty (Hier & Greenberg, 2009; Lightman, Herd, & Mitchell, 2005; Marechal, 2015).

This article is based on preliminary research and findings from an ongoing larger research project. As such, many critical questions remain that the research aims to address by exploring issues pertaining to the surveillance, criminalization, and governance through anti-fraud software of those receiving social assistance.


The author gratefully acknowledges the generous support of the Vanier Graduate Scholarship, which made this research possible.


  1. According to its website, Indue (2018) “provides clients with a monthly report that highlights the number of accounts requiring further information, alerts processed, and events considered suspicious. Additional reporting is available at the client’s request. For example, data mining for specific transaction types or sorting information stored in the data warehouse to reveal patterns for technical or marketing purposes” (n.p.). 
  2. The Australian Council of Social Services (2017) estimates the welfare card trial cost $18.9 million.


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