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Empty Streets, Busy Internet: A Time-Series Analysis of Cybercrime and Fraud Trends During COVID-19

Kemp, Steven ; Buil-Gil, David ; Moneva, Asier ; Miró-Llinares, Fernando ; Díaz-Castaño, Nacho (2021) — Journal of Contemporary Criminal Justice

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Synopsis

This publication examines how the COVID-19 pandemic and related lockdowns may have reshaped cybercrime and fraud opportunities in the United Kingdom. The authors pose questions about whether increases in cybercrime and fraud during the early pandemic period exceed normal crime variability and whether different fraud types and victim groups were affected in distinct ways. They use univariate ARIMA time-series models fitted to crime reports submitted to Action Fraud from April 2017 through March 2020 to generate 95% prediction intervals for April–July 2020, allowing them to assess whether observed counts exceeded historical expectations. They also supplement crime data with routine-activities indicators (e.g., online shopping, air travel, cinema attendance) to contextualize shifts in opportunities and victims. The results indicate that total cybercrime and total fraud in the UK rose above forecasted levels in the spring of 2020, with cybercrime peaking in March–May and fraud peaking around May–June before returning within the predictive bounds by July. Among fraud types, online shopping fraud surged and remained elevated above prior predictions for a time, while dating fraud showed a pronounced but shorter-lived rise. Ticket fraud declined to near zero during the early pandemic months, and door-to-door fraud remained within predicted ranges. Victimization patterns differed by actor: trends for individual victims rose above expected levels, whereas organizational victimization generally did not, except for online shopping fraud where organizations showed a notable uptick. The authors acknowledge limitations, including reliance on officially reported data that may undercount actual crime and potential reporting changes during the pandemic. They argue for a crime-specific, opportunity-based approach to prevention and note that the return toward pre-COVID activity levels may influence future cybercrime dynamics, highlighting the role of telework as a guardian and the need for targeted policy and practice responses.

Identified Gaps

The paper identifies a lack of time-series analysis of cybercrime and fraud during COVID-19. It also highlights uncertainty about why individual and organizational trends diverged, including possible differences in victimization, detection, and reporting. The authors call for greater offense, victim, and country specificity rather than broad claims about pandemic-related cybercrime increases.

Methods

The study analyzed monthly Action Fraud reports in the United Kingdom from April 2017 to July 2020. It used univariate ARIMA models, selected through a Hyndman-Khandakar stepwise algorithm using AICc, to forecast April-July 2020 counts from pre-lockdown data and compare observed reports with 95% prediction intervals. Analyses covered total cybercrime, total fraud, four fraud types, and individual versus organizational victims. Routine-activity indicators were descriptively compared with fraud trends.

Limitations

The study relies on police-reported Action Fraud data, while fraud and cybercrime reporting is low and therefore leaves a substantial unobserved dark figure. Changes in crime-recording practices over time could distort historical trend analysis. The dataset excluded reports without valid postcodes, and the proportion excluded was unknown. Organizational victim samples were too small to estimate models for dating, ticket, and door-to-door fraud.

Future Work

Examine organizational characteristics associated with cybercrime and fraud victimization during COVID-19; distinguish actual victimization changes from reporting and detection changes; assess whether homeworking shifted risk from organizations to under-protected individuals; and investigate whether existing offenders intensified activity or new actors entered fraud markets during the early pandemic.

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