Cryptocurrencies and future financial crime

Background Cryptocurrency fraud has become a growing global concern, with various governments reporting an increase in the frequency of and losses from cryptocurrency scams. Despite increasing fraudulent activity involving cryptocurrencies, research on the potential of cryptocurrencies for fraud has not been examined in a systematic study. This review examines the current state of knowledge about what kinds of cryptocurrency fraud currently exist, or are expected to exist in the future, and provides comprehensive definitions of the frauds identified. Methods The study involved a scoping review of academic research and grey literature on cryptocurrency fraud and a 1.5-day expert consensus exercise. The review followed the PRISMA-ScR protocol, with eligibility criteria based on language, publication type, relevance to cryptocurrency fraud, and evidence provided. Researchers screened 391 academic records, 106 of which went on to the eligibility phase, and 63 of which were ultimately analysed. We screened 394 grey literature sources, 128 of which passed on to the eligibility phase, and 53 of which were included in our review. The expert consensus exercise was attended by high-profile participants from the private sector, government, and academia. It involved problem planning and analysis activities and discussion about the future of cryptocurrency crime. Results The academic literature identified 29 different types of cryptocurrency fraud; the grey literature discussed 32 types, 14 of which were not identified in the academic literature (i.e., 47 unique types in total). Ponzi schemes and (synonymous) high yield investment programmes were most discussed across all literature. Participants in the expert consensus exercise ranked pump-and-dump schemes and ransomware as the most profitable and feasible threats, though pump-and-dumps were, notably, perceived as the least harmful type of fraud. Conclusions The findings of this scoping review suggest cryptocurrency fraud research is rapidly developing in volume and breadth, though we remain at an early stage of thinking about future problems and scenarios involving cryptocurrencies. The findings of this work emphasise the need for better collaboration across sectors and consensus on definitions surrounding cryptocurrency fraud to address the problems identified.

More crime in cities? On the scaling laws of crime and the inadequacy of per capita rankings—a cross-country study

Crime rates per capita are used virtually everywhere to rank and compare cities. However, their usage relies on a strong linear assumption that crime increases at the same pace as the number of people in a region. In this paper, we demonstrate that using per capita rates to rank cities can produce substantially different rankings from rankings adjusted for population size. We analyze the population–crime relationship in cities across 12 countries and assess the impact of per capita measurements on crime analyses, depending on the type of offense. In most countries, we find that theft increases superlinearly with population size, whereas burglary increases linearly. Our results reveal that per capita rankings can differ from population-adjusted rankings such that they disagree in approximately half of the top 10 most dangerous cities in the data analyzed here. Hence, we advise caution when using crime rates per capita to rank cities and recommend evaluating the linear plausibility before doing so.

Offline crime bounces back to pre-COVID levels, cyber stays high: interrupted time-series analysis in Northern Ireland

Much research has shown that the first lockdowns imposed in response to the COVID-19 pandemic were associated with changes in routine activities and, therefore, changes in crime. While several types of violent and property crime decreased immediately after the first lockdown, online crime rates increased. Nevertheless, little research has explored the relationship between multiple lockdowns and crime in the mid-term. Furthermore, few studies have analysed potentially contrasting trends in offline and online crimes using the same dataset. To fill these gaps in research, the present article employs interrupted time-series analysis to examine the effects on offline and online crime of the three lockdown orders implemented in Northern Ireland. We analyse crime data recorded by the police between April 2015 and May 2021. Results show that many types of traditional offline crime decreased after the lockdowns but that they subsequently bounced back to pre-pandemic levels. In contrast, results appear to indicate that cyber-enabled fraud and cyber-dependent crime rose alongside lockdown-induced changes in online habits and remained higher than before COVID-19. It is likely that the pandemic accelerated the long-term upward trend in online crime. We also find that lockdowns with stay-at-home orders had a clearer impact on crime than those without. Our results contribute to understanding how responses to pandemics can influence crime trends in the mid-term as well as helping identify the potential long-term effects of the pandemic on crime, which can strengthen the evidence base for policy and practice.

Identifying seasonal spatial patterns of crime in a small northern city

Objectives To explore spatial patterns of crime in a small northern city, and assess the degree of similarity in these patterns across seasons. Methods Calls for police service frequently associated with crime (theft, break and enter, domestic dispute, assault, and neighbor disputes) were acquired for a five year time span (2015–2019) for the city of North Bay, Ontario, Canada (population 50,396). Exploratory data analysis was conducted using descriptive statistics and a kernel density mapping technique. Andresen’s spatial point pattern test (SPPT) was then used to assess the degree of similarity between the seasonal patterns (spring, summer, autumn, winter) for each call type at two different spatial scales (dissemination area and census tract). Results Exploratory data analysis of crime concentration at street segments showed that calls are generally more dispersed through the city in the warmer seasons of spring and summer. Kernel density mapping also shows increases in the intensity of hotspots at these times, but little overall change in pattern. The SPPT does find some evidence for seasonal differences in crime pattern across the city as a whole, specifically for thefts and break and enters. These differences are focused on the downtown core, as well as the outlying rural areas of the city. Conclusions For the various crime types examined, preliminary analysis, kernel density mapping, and the SPPT found differences in crime pattern consistent with the routine activities theory.

The impact of the COVID-19, social distancing, and movement restrictions on crime in NSW, Australia

The spread of COVID-19 has prompted Governments around the world to impose draconian restrictions on business activity, public transport, and public freedom of movement. The effect of these restrictions appears to vary from country to country and, in some cases, from one area to another within a country. This paper examines the impact of the COVID-19 restrictions imposed in New South Wales (NSW) by the State Government. We examine week-to-week changes in 13 categories of crime (and four aggregated categories) from 2 January 2017 to 28 June 2020. Rather than using the pre-intervention data to make a forecast and then comparing that with what is actually observed, we use a Box–Jenkins (ARIMA) approach to model the entire time series. Our results are broadly in accord with those of other studies, but we find no effect of the lockdown (upward or downward) on domestic assault.

The new normal of web camera theft on campus during COVID-19 and the impact of anti-theft signage

Objective The opportunity for web camera theft increased globally as institutions of higher education transitioned to remote learning during COVID-19. Given the thousands of cameras currently installed in classrooms, many with little protection, the present study tests the effectiveness of anti-theft signage for preventing camera theft. Methods Examined web camera theft at a southern, public university located in the United States of America by randomly assigning N = 104 classrooms to receive either anti-theft signage or no signage. Camera theft was analyzed using Blaker’s exact test. Results Classrooms not receiving anti-theft signage (control) were 3.42 times more likely to exhibit web camera theft than classrooms receiving anti-theft signage (medium effect size). Conclusions Using classrooms as the unit of analysis presents new opportunities for not only future crime prevention experiments, but also improving campus safety and security. Also, preventing web camera theft on campus is both fiscally and socially responsible, saving money and ensuring inclusivity for remote learners.

The effect of the COVID-19 pandemic on mental health calls for police service

Drawing upon seven years of police calls for service data (2014–2020), this study examined the effect of the COVID-19 pandemic on calls involving persons with perceived mental illness (PwPMI) using a Bayesian Structural Time Series. The findings revealed that PwPMI calls did not increase immediately after the beginning of the pandemic in March 2020. Instead, a sustained increase in PwPMI calls was identified in August 2020 that later became statistically significant in October 2020. Ultimately, the analysis revealed a 22% increase in PwPMI calls during the COVID-19 pandemic than would have been expected had the pandemic not taken place. The delayed effect of the pandemic on such calls points to a need for policymakers to prioritize widely accessible mental health care that can be deployed early during public health emergencies thus potentially mitigating or eliminating the need for increased police intervention, as was the case here.

A victim-centred cost–benefit analysis of a stalking prevention programme

Research suggests that stalking inflicts great psychological and financial costs on victims. Yet costs of victimisation are notoriously difficult to estimate and include as intangible costs in cost–benefit analysis. This study reports an innovative cost–benefit analysis that used focus groups with multi-agency teams to collect detailed data on operational resources used to manage stalking cases. This method is illustrated through the presentation of one case study. Best- and worst-case counterfactual scenarios were generated using the risk assessment scores and practitioner expertise. The findings suggest that intervening in high-risk stalking cases was cost-beneficial to the state in all the case studies we analysed (even if it incurs some institutional costs borne by the criminal justice system or health) and was often cost-beneficial to the victims too. We believe that this method might be useful in other fields where a victim- or client-centred approach is fundamental.

The impact of strict measures as a result of the COVID-19 pandemic on the spatial pattern of the demand for police: case study Antwerp (Belgium)

COVID-19 impacts the daily lives of millions of people. This radical change in our daily activities affected many aspects of life, but acted as well as a natural experiment for research into the spatial distribution of 911 calls. We analyse the impact of the COVID-19 measures on the spatial pattern of police interventions. Crime is not uniformly distributed across street segments, but how does COVID-19 affect these spatial patterns? To this end, Gini coefficients are calculated and a proportion differences spatial point pattern test is applied to compare the similarity of the patterns of incidents before, during, and after the first lockdown in Antwerp, Belgium. With only essential mobility being allowed, the emergency call pattern has not significantly changed before, during or after this lockdown, however, a qualitative shift in police officer’s daily work may have had an effect on the daily operation of the Antwerp police force.

In the post-COVID-19 era, is the illegal wildlife trade the most serious form of trafficking?

Despite the immense impact of wildlife trafficking, comparisons of the profits, costs, and seriousness of crime consistently rank wildlife trafficking lower relative to human trafficking, drug trafficking and weapons trafficking. Using the published literature and current events, we make the case, when properly viewed within the context of COVID-19 and other zoonotic diseases transmitted from wildlife, that wildlife trafficking is the most costly and perhaps the most serious form of trafficking. Our synthesis should raise awareness of the seriousness of wildlife trafficking for humans, thereby inducing strategic policy decisions that boost criminal justice initiatives and resources to combat wildlife trafficking.