White Collar Crime and Information Technology: Overview
This paper provides an overview of white collar crime and examines how information technology (IT) has expanded the scope and scale of these offenses. Beginning with Edwin Sutherland's foundational definition, the paper traces the sociological and legal dimensions of white collar crime before identifying five key IT-facilitated methods: logic bombs, data diddling, trapdoor and salami techniques, skimming, and Trojan horses. The paper also analyzes how the current U.S. legal framework inadequately punishes white collar offenders, the risk that illicit proceeds fund terrorist organizations, and what steps — particularly removing criminal opportunity — can reduce the prevalence of these crimes.
- Introduction: Scope and purpose of the paper
- Overview of White Collar Crime: Definitions, history, and the fraud triangle
- White Collar Crimes and Information Technology: How IT expands criminal opportunity
- Common IT-Facilitated Embezzlement Methods: Five specific computer-assisted crime techniques
- Current Legal Framework for White Collar Crime in the U.S.: Legal gaps and inadequate punishment regimes
- Conclusion: Summary and call for legal and structural reform
✍️ How to write this paper — guide, tools & examples ▾
What makes this paper effective
- The paper grounds its argument in an authoritative historical definition — Sutherland's 1939 coinage — and layers legal, sociological, and technological perspectives on top of it, giving the analysis both depth and coherence.
- Concrete enumeration of five IT-enabled embezzlement methods (logic bombs, data diddling, salami techniques, skimming, and Trojan horses) provides specific, actionable detail that supports the broader theoretical argument.
- The fraud triangle framework is introduced visually and then referenced analytically across multiple sections, creating a structural throughline that ties the paper together.
Key academic technique demonstrated
The paper demonstrates effective use of a conceptual framework — the fraud triangle — as an organizing device. Rather than simply describing criminal behaviors, the author maps them onto a pre-existing explanatory model (opportunity, pressure, rationalization) and returns to that model when discussing both causes and policy solutions. This technique shows how academic frameworks can unify descriptive and prescriptive content within a single argument.
Structure breakdown
The paper opens with a scoped introduction that previews all major sections. It then moves through three analytical sections — defining white collar crime sociologically and legally, cataloguing IT-enabled methods, and assessing the legal response — before concluding with a call to address the opportunity pillar of the fraud triangle. The structure is linear and cumulative, with each section building on the last.
Introduction
Given the virtual ubiquity of information technology (IT) today, it is not surprising that white collar crime using these resources has assumed new importance and relevance (Dervan, 2014). As discussed further below, white collar crimes are by definition nonviolent in nature, but the enormous sums derived from such unlawful activities are increasingly being diverted to fund violent terrorist organizations that target the interests of the United States at home and abroad (Ndubueze & Igbo, 2016; Priyatno, 2017). The purpose of this paper is to provide an overview of white collar crime in general and how IT has been used to perpetrate these crimes in recent years in particular. In addition, an examination is provided concerning the various types of white collar crimes committed using IT and how they are facilitated through collaboration between mainstream business practitioners and the criminal underworld. Finally, a discussion of how the current legal framework in the U.S. may inadvertently encourage white collar crimes by high-level executives, along with corresponding recommendations, will conclude the study.
References
Arya, A. & Sun, H. L. (2011, January 1). Stock option backdating at Comverse Technology: Ethical, regulatory, and governance issues. Journal of the International Academy for Case Studies, 17(1), 55–62.
Asner, M. A. (2015, February 3). White-collar crime, banks are more often prey than predator. American Banker, 1(17), 13.
Black's law dictionary. (1990). St. Paul, MN: West Publishing Company.
Ball, T. (2015, January 1). International tax compliance agreements and Swiss bank privacy law: A model protecting a principled history. The George Washington International Law Review, 48(1), 233–240.
Cliff, G. & Wall-Parker, A. (2017, April). Statistical analysis of white collar crime. Criminology and Criminal Justice. Retrieved from http://criminology.oxfordre.com/view/10.1093/acrefore/9780190264079.001.0001/acrefore-9780190264079-e-267.
Dervan, L. E. (2011, December). International white collar crime and the globalization of internal investigations. Fordham Urban Law Journal, 39(2), 361–365.
Eichenwald, K. (2015, May 8). The 'flash crash' case doesn't add up; the government's case against "Mr. Flash Crash" is a misguided sham. Newsweek, 164(18), 37.
Goldfarb, R. (2018, Winter). No big-game hunting at Justice: How federal prosecutors let major white-collar criminals off the hook and stick shareholders with the costs of corporate crime. The American Prospect, 29(1), 110–114.
Ndubueze, P. N. & Igbo, E. U. (2016, July–December). Cybercrime victimization among internet-active Nigerians: An analysis of socio-demographic correlates. International Journal of Criminal Justice Sciences, 8(2), 225–234.
Priyatno, D. (2017, January). The alternative model of corporate criminal sanction management in Indonesia. Journal of Legal, Ethical and Regulatory Issues, 20(1), 37–41.
van Wyk, B. (2017, June). How technology can help combat payroll fraud in Africa. African Business, 442, 40–43.
What is computer embezzlement? (2018). Geigle Law Firm. Retrieved from
Already a member? Log in
Unlock the rest of this paper
135,000+ research papers · AI writing tools · Plagiarism & AI detection
7-Day Pass
Does not renew
Get 7-Day PassMonthly
Renews at $12.99/month until canceled
Start MonthlyAnnual
Renews at $99/year until canceled
Start Annual- Unlimited AI writing tools
- Plagiarism and AI text detection tool
Plan details
Unlimited AI writing tools are for individual, non-automated use and are subject to our Terms of Service and abuse-prevention measures.
TextChecker scans: 3 during the 7-Day Pass, or 5 per month with Monthly and Annual.
Prices exclude applicable tax.
Always verify citation format against your institution’s current style guide requirements.