Employment scams: a crime script and scoping review of prevailing methods, trends and future directions Anran Sun, Eray Arda Akartuna, Matthew Manning Trends in Organized Crime, 2026 Employment scams, where victims are defrauded by applying to fake or deceptive job advertisements, have become a growing concern since Covid-19. Many jurisdictions, especially financial centres such as the United States, Hong Kong and Singapore, have reported year-on-year increases in employment-related deception. Economic pressures from the pandemic, as well as the growth of industrialised scam compounds in Southeast Asia that rely on labour trafficking, have accelerated this growing crime problem. Academic research on this trend, particularly its detection through algorithmic pattern detection, has existed for some time but remains scattered across different disciplines. A holistic structured analysis of existing research, and our current scholarly understanding of this issue and its detection, is therefore necessary to inform future approaches and priorities. This study has two complementary aims: first, it conducts an exploratory PRISMA-ScR-compliant scoping review that analyses 16 research articles relating to the commission and detection of employment scams. Second, the insights gained are used to construct a crime script, which provides a holistic understanding of how employment scams occur and possible points of intervention. Using the findings from both the review and the script, the paper analyses key findings in contemporary employment scam research, key scam typologies, prevailing research limitations, available datasets/methods and useful avenues for future study. The aim of this paper is to provide a unified overview of the current state of the art of both employment scams and current research, while defining the next best steps to support the prevention of this worsening crime trend.
The Global Dirty Laundry: A Heckman-Adjusted Gravity Model of Illicit Financial Flows Andreas Chai, Matthew Manning, Elena Stepanova Journal of Quantitative Criminology, 2026 This study aims to identify key factors that shape the global network of illicit financial flows (IFFs) related to money laundering and other financial crimes. Specifically, it examines the factors determining both i) the selection of destination countries and ii) the volume of illicit funds laundered. We developed a Heckman-adjusted gravity model of illicit financial flows, utilizing data from Suspicious Activity Reports lodged between 2007 to 2017. The first stage of the model analyses the selection of destination countries, while the second stage estimates the volume of laundered funds. Key variables include GDP, financial service quality, Egmont membership, corruption levels, and geographic distance. The Heckman correction is applied to address selection bias. Our findings indicate that wealthier countries attract higher levels of illicit financial flows. However, high quality financial services deter both the selection of a country for laundering and the volume of funds laundered. Reported IFFs are more likely from countries with high corruption and conflict levels. Trade, culture and geographic proximity are also found to be correlated with the likelihood and magnitude of reported IFFs. Results show evidence of displacement and provide evidence of the link between illicit financial flows and the international flow of trade, people and remittances. Limitations include potential biases in the data and the exclusion of non-USD transactions.
The perceived risk of cybercrime victimisation and behavioural adaptations: Results from an Australian representative survey Gabriel TW Wong, Matthew Manning, Chau-kiu Cheung Journal of Criminology, 2025 Drawing on data from the Life in Australia™ panel (ANUpoll Wave 31; n = 1,911), this study investigates the factors that shape individuals’ perceived increase of cybercrime victimisation risk and how these perceptions influence their online disclosure behaviour. Using ordered probability and partial proportional odds models, we examine the role of personal attributes, individual safety concerns, perceived capability to avoid cybercrime, and perceived capability of institutional guardianship. The study provides a unique contribution by employing recent nationally representative data from all Australian states and territories to analyse multiple dimensions of cyber-related concerns. We also use a detailed categorisation of specific cybercrime types to predict both the perceived increase of risk and preventative behavioural adaptations. Guided by Ferraro’s risk interpretation model and Beck’s risk society paradigm, we find that personal attributes and concern factors differentially shape individuals’ perceptions of increasing cybercrime risk. Concerns about identity crime and misuse, online goods fraud, banking fraud, and malicious software significantly heighten perceived increases in risk. These perceptions are further influenced by trust in online security systems and public data guardianship, consistent with the broader concept of institutional guardianship. Overall, the findings show that diminished confidence in digital and institutional safeguards predicts stronger perceptions of increasing cybercrime risk and greater caution in personal information disclosure.
Shifting routines and the industrialisation of scams: the impact of Covid-19 on deception crimes in Hong Kong Eray Arda Akartuna, Felix Sin Wai Yeung, Matthew Manning, Alexandre Bish Trends in Organized Crime, 2025 We investigate the impact of the Covid-19 pandemic on “deception” (predominantly fraud) crime rates in Hong Kong, analysing the impact of specific restrictions imposed between February 2020 and June 2023. We use an Auto ARIMA time series model to compare pre-pandemic and pandemic rates of deception. During the pandemic months, we assess changes to the deception rate based on the imposition and relaxing of Covid policies, including lockdowns, quarantine requirements, travel restrictions and financial stimulus payments. Similar to existing literature, we find that deception crimes increased during the pandemic and were always above the upper bound of ARIMA-forecasted rates of deception crimes during those months. However, contrary to findings from earlier studies during the pandemic itself, we find that periods of relaxed policies – particularly the easing of lockdown and travel restrictions – were in fact associated with higher rates of deception crimes. We consider our findings through the lens of routine activity theory and crime displacement theory. Drawing on existing studies, we provide explanations for identified trends, pointing to the shifting nature of both offender and victim routines, such as increased phone and social media usage, both during and post-pandemic. Specifically, we emphasise the industrialisation of deception (so-called “pig butchering”) in dedicated scam compounds across Southeast Asia, which engage in labour trafficking (made easier in months of eased lockdown/travel restrictions) to upscale their operations. We consider our explanations from the perspectives of routine adaptability and resilience, which we motivate as a theoretical contribution for assessing the crime implications of disruptive events more widely.
Triad influence on the detection of crime in Hong Kong Gabriel Wong, Matthew Manning, T. Wing Lo, Shane D. Johnson Plos One, 2024 We use bootstrap data envelopment analysis, adjusting for endogeneity, to examine police efficiency in detecting crime in Hong Kong. We address the following: (i) is there a correlation between the detection of crime and triad influence? (ii) does the level of triad influence affect the efficiency in translating inputs (police strength) into outputs (crime detection)? and (iii) how can the allocation of policing resources be adjusted to improve crime detection? We find that nighty-eight percent of Hong Kong police districts in our sample were found to be inefficient in the detection of crime. Variation was found across districts regarding the detection of violent, property and other crimes. Most inefficiencies and potential improvements in the detection of crime were found in the categories violent and other crimes. We demonstrate how less efficient police districts can modify police resourcing decisions to better detect certain crime types while maintaining current levels of resourcing. Finally, we highlight how the method we outline improves efficiency estimation by adjusting for endogeneity and measuring the conditional efficiency of each district (i.e. the efficiency of crime detection taking the instrumental variables (e.g. influence of triads) into consideration). The use of frontier models to assist in evaluating policing performance can lead to improved efficiency, transparency, and accountability in law enforcement, ultimately resulting in better public safety outcomes and publicly funded resource allocation.
INSIDER THREAT: A Systemic Approach Pierre Skorich, Matthew Manning Insider Threat A Systemic Approach, 2024 Establishing a new framework for understanding insider risk by focusing on systems of organisation within large enterprises, including public, private, and not-for-profit sectors, this book analyses practices to better assess, prevent, detect, and respond to insider risk and protect assets and public good. Analysing case studies from around the world, the book includes real-world insider threat scenarios to illustrate the outlined framework in the application, as well as to assist accountable entities within organisations to implement the changes required to embed the framework into normal business practices. Based on information, data, applied research, and empirical study undertaken over ten years, across a broad range of government departments and agencies in various countries, the framework presented provides a more accurate and systemic method for identifying insider risk, as well as enhanced and cost-effective approaches to investing in prevention, detection, and response controls and measuring the impact of controls on risk management and financial or other loss. Insider Threat: A Systemic Approach will be of great interest to scholars and students studying white-collar crime, criminal law, public policy and criminology, transnational crime, national security, financial management, international business, and risk management.
Editorial: Economic evaluation in evidence-based criminal justice contexts Susan Giles, Siddhartha Bandyopadhyay, Karen Shalev, Matthew Manning Frontiers in Psychology, 2024 In times of economic austerity, criminal justice agencies are required to make evidence-based decisions that yield optimal return. The aim of this inter-disciplinary special issue is to showcase economic analysis taking place in policing and criminal justice contexts, using both established and innovative techniques. It is hoped that this will contribute a robust evidence base alongside demonstrating innovation in economic methodologies that could be beneficial to other researchers. Four quality publications were received, demonstrating innovative economic practices in estimating treatment effects or directing resources to high-risk individuals.Understanding the effectiveness and cost effectiveness of a perpetrator intervention programme.Domestic violence is a pervasive phenomenon for which a number of within and across generational negative impacts are assessed. Yet the evidence base around what works is still patchy and economic analysis of interventions is particularly limited. Karavias and colleagues consider the impact of a so-called ‘batterer intervention programme’ (BIP) called CARA (Cautioning and Relationship Abuse) and find a strong reduction in reoffending among those who attended the programme across two police force areas with very different socio-economic and demographic characteristics. Their impact evaluation naturally leads to an economic evaluation quantifying the benefit achieved in monetary terms. It indicates the monetised benefit of the intervention ranges from £2.75-11.1 per pound spent. This strongly suggests that CARA will deliver benefits by reducing reoffending and be economically efficient if rolled out across more police forces. The use of machine learning methods to identify the most important variables that determine treatment selection and being able to use boundedness tests to show that unobservable factors would need to have a dramatic impact to invalidate the results provide robustness to the analysis.Incorporating impact heterogeneity into cost-benefit analysis.Traditional cost-benefit analyses (CBA) rely on average treatment effects and do not consider contextual factors that moderate outcomes for community sub-groups. Existing gains, as a result, may disproportionately target and benefit certain subgroups. This is problematic for criminal justice interventions where poverty and access to justice may influence outcomes. Manning and colleagues consider how justice processes treat different groups and whether CBA can be enhanced by the inclusion of such heterogeneity. Drawing upon similar past research, an economic framework is suggested including quantile treatment effects and a range of moderators (e.g. ethnicity, gender, latent constructs, exclusion, and governance). The enhanced CBA APP is demonstrated using existing data from a school-based intervention in Australia (Manning et al., 2016). Future developments, including machine learning, are then considered. The current research offers considerable methodological innovation. By moving away from average treatment effects and overall societal benefit, the enhanced CBA APP potentially improves the accuracy of resource allocation so that finite resources are directed more equitably. It can help achieve maximum economic and social outcomes whilst targeting unequal treatment and outcomes for vulnerable and excluded social groups.Offence prioritisation in high-volume, high-harm crimesGiles and colleagues discuss the pervasive risk or harm posed by online child sexual abuse, which strains law enforcement’s ability to respond effectively. Whilst prioritisation methods exist for individuals with experience of offline offences (KIRAT; Long et al, 2015), there is a lack of focus on online-only offences (OOCSA), partly due to ambiguity regarding victim harm and online offending’s contribution to it. Giles and colleagues produce a narrative review to address this gap, identifying five themes from existing literature: problems defining OOCSA, normalizing online harm, OOCSA grooming processes, comparisons with offline abuse, and the mechanisms between OOCSA and harm. They suggest factors like shame, reach of abuse, image permanence, victim vulnerability, and social support could guide prioritization strategies. Drawing upon original police data, crime reports and surveys they estimate the economic burden of OOCSA in England and Wales. Adapting UK Home Office figures to OOCSA (Radakin et al, 2021) they establish lifetime costs (£7.4 million based on police reports), scaling up to consider undetected crimes (£59.6 million) and national prevalence (£1.4 billion from self-report surveys). This research highlights the potential for economic models in understanding and addressing novel areas like OOCSA, providing insights for future researchers and law enforcement to develop evidence-led tools and strategies. An economic evaluation of restorative justice post sentence in England and WalesParticipation in restorative justice interventions post-sentence has been shown to reduce reoffending and mitigate harm to victims. Investment in, and access to, restorative justice remains limited in England and Wales. Focusing on direct and indirect restorative justiceinterventions for victims and offenders post-sentence in England and Wales, Grimsey Jones and colleagues developed a model to estimate the social benefit–cost ratio of restorative justice, as well as the direct financial return to the criminal justice system. Their estimates suggest that increasing the proportion of eligible cases referred for a restorative justice intervention from 15 to 40% could be associated with an increase in investment of £5 m, and benefits to the criminal justice system totaling £22 m, implying a net saving of £17 m. The economic case for investment in restorative justice centers on identifying offenders with a high risk of offending and enabling them to participate in an intervention that has been repeatedly demonstrated to help them to change their behavior. The study can help advance policymakers’ understanding of the value of restorative justice as well as how to harness this value to benefit victims, offenders and society. Summary contributionsEach paper has contributed new knowledge that will enhance the rigor and external validity of economic models. The fact that we received only four papers for this special issue, despite having an extended timeline, is a testament to the time it takes to produce high-quality economic evaluations. Its relative scarcity is sometimes further compromised by data issues and a reluctance to venture into an area that is not set up to appropriately measure the economic costs and benefits. The papers however demonstrate that with advances in methodology, if appropriate data were routinely collected, robust economic analysis can be undertaken providing robust evidence on the effective use of scarce resources. We hope that readers can derive benefits from the innovations presented here.
The welfare cost of terrorism Margarita Vorsina, Matthew Manning, Christopher M. Fleming, Christopher L. Ambrey, Christine Smith Terrorism and Political Violence, 2017
Preventing violence in seven countries: Global convergence in policies Marianne Junger, Lynette Feder, Joy Clay, Sylvana M. Côté, David P. Farrington, Kate Freiberg, Vicente Garrido Genovés, Ross Homel, Friedrich Lösel, Matthew Manning, Paul Mazerolle, Rob Santos, Martin Schmucker, Christopher Sullivan, Carole Sutton, Tom van Yperen, Richard E. Tremblay European Journal on Criminal Policy and Research, 2007
