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Computer assisted frauds: An examination of offender and offense characteristics in relation to arrests

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Abstract

Previous studies on fighting computer-assisted frauds have attempted to assist law enforcement agencies (LEAs) to better understand important aspects of motivation, opportunity and deterrence. However, there have been few empirical studies on the profiles of convicted offenders, post detection. This paper examines characteristics of frauds and their associated respective law enforcement response with particular emphasis on frauds facilitated by information technology. The findings show how the prosecution and conviction of the offenders differ among commonly-seen types of computer assisted frauds, and bring new evidence to the common association of gender and crime, severity and punishment, etc. The findings may help LEAs and legislative bodies to evaluate their current practices from the point of restorative social justice.

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Notes

  1. http://www.telegraph.co.uk/news/2016/07/21/one-in-people-now-victims-of-cyber-crime/

  2. https://www.theguardian.com/technology/2013/oct/30/online-fraud-costs-more-than-100-billion-dollars

  3. https://www.juniperresearch.com/press/press-releases/cybercrime-cost-businesses-over-2trillion

  4. https://www.scamwatch.gov.au/types-of-scams/unexpected-money/nigerian-scams

  5. https://www.ali.org/publications/show/model-penal-code

  6. Federal Bureau of Investigation. “Ten-Year Arrest Trends, by Sex, 2003–2012”.

  7. In Section 2.2, ‘Classifying Offenses’, under NIBRS User Manual.

  8. The NIBRS User Manual contains detailed definition and examples of Group A offense and Group B offense.

  9. SAS 9.2 User’s Guide, Second Edition

  10. Note – Typically, offenders are above 14 years of age.

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Authors and Affiliations

  1. Department of Management Science and Systems, State University of New York at Buffalo, 301 Alfiero Center, Buffalo, NY, 14260, USA

    Ruochen Liao, Shenaz Balasinorwala & H. Raghav Rao

  2. Blue Cross Blue Shield of WNY, 257 West Genesee Street, Buffalo, NY, 14202, USA

    Ruochen Liao, Shenaz Balasinorwala & H. Raghav Rao

  3. Department of ISCS, College of Business, University of Texas at San Antonio, San Antonio, TX, USA

    Ruochen Liao, Shenaz Balasinorwala & H. Raghav Rao

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  1. Ruochen Liao
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  2. Shenaz Balasinorwala
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  3. H. Raghav Rao
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Correspondence to Ruochen Liao.

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Funding

This research has been funded in part by the National Science Foundation under grants # 1523174, #1554373, 1,227,353 and # 1419856. We acknowledge the comments of the guest editors and referees that have considerably increased the clarity of the paper.

Appendix

Appendix

We tested Spearman rank correlation for characteristics of the offender and characteristics of the offense. Including the dependent variable (probability of arrest), all independent variables are binary except for age. The result listed here indicate all other variables are well below the .5 threshold for multicollinearity problem to run regression except for centered age and centered age square. Having a high multicollinearity is normal between a variable and its quadratic term, and by employing centering, multicollinearity is brought down to .5, which is considered to be an acceptable level (Allison 2012).

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Liao, R., Balasinorwala, S. & Raghav Rao, H. Computer assisted frauds: An examination of offender and offense characteristics in relation to arrests. Inf Syst Front 19, 443–455 (2017). https://doi.org/10.1007/s10796-017-9752-4

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