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Making the most of cybercrime and fraud crime report data: a case study of UK Action Fraud
International Journal of Population Data Science, Volume: 7, Issue: 1
Swansea University Author: Sara Correia-Hopkins
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IntroductionResearchers and public authorities are increasingly exploring the potential of administrative data to generate new insights. This includes recent work leveraging the opportunities of the crime report data collected by the UK’s national reporting centre Action Fraud (AF). However, the qua...
|Published in:||International Journal of Population Data Science|
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IntroductionResearchers and public authorities are increasingly exploring the potential of administrative data to generate new insights. This includes recent work leveraging the opportunities of the crime report data collected by the UK’s national reporting centre Action Fraud (AF). However, the quality of these data and its implications for data users have not been systematically analysed.ObjectivesThis paper outlines challenges and opportunities of using AF data in cybercrime and fraud victimisation research and practice and makes recommendations to improve the quality of this dataset.MethodsThe author has undertaken two studies using samples of AF data pertaining to crime reports within the Welsh police forces, between 2014 and 2020. Quality diagnostic checks, reflections and methodological decisions were considered across each study. These were reviewed, key themes were identified and discussed with data provider representatives and a broader group of researchers to finalise the recommendations presented.ResultsThe strengths and limitations of AF data are discussed and grouped into themes, closely aligned with four quality dimensions widely used by statistical authorities. This includes an assessment of 1) the impact of under-reporting and 2) the purpose and rules of crime recording, on the relevance of the data to its users; 3) the accuracy and reliability of the data; 4) the consistency of recording and its impact on coherence and comparability; and 5) the accessibility and timeliness of the data.ConclusionsRecommendations are made to improve AF data to generate better quality insights across the dimensions of relevance, accuracy & reliability, coherence & comparability and the accessibility & timeliness of this dataset. Additionally, a data catalogue would enable frontline officers and researchers to make the most of this dataset, harnessing it to produce key insights for crime prevention, investigation, and victim support.
Faculty of Humanities and Social Sciences