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The Identification of Ponzi Schemes

Can a Picture Tell a Thousand Frauds

Bibliographic Data

ID14970918
AuthorsJacqueline M Drew (0000-0001-5586-7873, Griffith University, corresponding author), Michael E Drew (0000-0002-2822-9611)
Year2010
Volume19
Issue1
Pages51-70
Publication date2010-01-01
Peer ReviewedYes
Open AccessNo
TypeARTICLE
VenueGriffith Law Review (JOURNAL)
Journal identifiersISSN: 1038-3441 • E-ISSN: 1839-4205
PublisherInforma UK Limited (PUBLISHER • GB)
DOI10.1080/10854668.2010.10854668
OpenAlexW1506425691
LanguageEN
Citations received2
References cited4

There is voluminous commentary on the origins of the global financial crisis (GFC), international attempts to limit the contagion and the Herculean effort to stop the global economy sliding into a depression. However, in the fast-moving world of the GFC, the debate shifted to the search for answers to the most challenging question: can we stop this from occurring again? To date, a number of responses have been formulated, including the need for a more holistic approach to regulating the global financial system, more stringent controls on banks and new financial products and reform of executive remuneration practices that encourage excessive risk-taking. This article suggests that an additional issue in the reform debate warrants consideration. The adequacy and implementation of fraud-detection systems in the financial services industry must be addressed. The monthly returns from the largest feeder fund in the US$65 billion Ponzi scheme overseen by Bernard L Madoff are analysed to demonstrate how the performance characteristics of investment schemes can be used as a potential red flag indicator in a broad system of fraud detection. It is argued that performance characteristic analysis is likely to play an important role as one tool within a collection of quantitative and qualitative assessment controls able to identify fraud perpetration in the financial services industry

Business · Economics · Financial crisis · Financial services · Great Depression · Identification (biology · Investment (military · Investment banking · Macroeconomics · Political science · Politics · Project commissioning · Publishing · Remuneration · Crime, Illicit Activities, and Governance · Cybercrime and Law Enforcement Studies · Law · Spam and Phishing Detection · Accounting · Finance

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  • Efficient tests for normality, homoscedasticity and serial independence of regression residuals

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Unique citing works2
Citations per year0.4
Citation span2021 - 2021 (1)
Citation velocityhistorical
Highly citedNo
Citation typesNeutral: 2

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