Abstract
Financial statement fraud is a costly problem for society and so better aids, such as detection models, are needed to make detection faster and more effective. The choice of which independent variables to use in such models is crucial, but surprisingly the selection process in prior research is not standardised. Consequently, a new framework is proposed for this, the Fraud Detection Triangle (FDT). The FDT adapts the well-known Fraud Triangle, which was originally designed to explain the drivers of fraudulent behaviour rather than its detection. Each original factor is extended, and a new one, Suspicious Information, is included.
| Original language | English |
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| Publication status | Published - 2016 |
| Event | Accounting and Finance Association of Australia and New Zealand (AFAANZ) 2016 - Jupiters Hotel, Gold Coast, Australia Duration: 3 Jul 2016 → 5 Jul 2016 http://www.afaanz.org/conferences |
Conference
| Conference | Accounting and Finance Association of Australia and New Zealand (AFAANZ) 2016 |
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| Abbreviated title | AFAANZ |
| Country/Territory | Australia |
| City | Gold Coast |
| Period | 3/07/16 → 5/07/16 |
| Internet address |
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Dive into the research topics of 'Improving models that detect financial statement fraud: A new framework to guide variable selection'. Together they form a unique fingerprint.Student theses
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Financial statement fraud detection using supervised learning methods
Author: Gepp, A., 10 Oct 2015Supervisor: Kumar, K. (Supervisor) & Bhattacharya, S. (External person) (Supervisor)
Student thesis: Doctoral Thesis
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