Addressing the problem of financial statement fraud: Better detection through improved models

Research output: Contribution to conferenceAbstractResearchpeer-review

Abstract

Purpose: This paper aims to facilitate the development of more effective models to detect fraud. Financial statement fraud continues to be a costly problem to society and detection of it is difficult. Better aids, such as fraud detection models, are needed to improve detection. The selection of explanatory variables is crucial to developing such models, and yet, surprisingly, the selection process in prior financial statement fraud detection studies is not standardised. This paper proposes a new framework for this purpose.

Design/methodology/approach: The new framework adapts the well-known Fraud Triangle, which was originally designed to explain drivers of fraudulent behaviour and needs to be adapted for use in fraud detection. The adaptation is done by the inclusion of a new Suspicious Information factor. This new framework also incorporates modifications suggested in prior research as extensions to the existing factors.

Findings: Publicly available variables to operationalise each factor of the new Fraud Detection Triangle (FDT) framework already exist and there is preliminary empirical support for each factor in the FDT framework. Research into additional variables that measure R and S factors would be beneficial as less focus has been placed on them in prior research.

Originality/Value/Practical Significance: The FDT is more suited to fraud detection than the previous fraud triangle. As a guiding theory for variable selection (previously done primarily on an ad hoc basis), it can facilitate the development of more accurate detection models. The variables analysed to operationalise the framework are also more comprehensive compared with prior research.

Original languageEnglish
Publication statusPublished - 2016
Event8th Asia-Pacific Interdisciplinary Research in Accounting Conference (APIRA) - RMIT University, Melbourne, Australia
Duration: 13 Jul 201615 Jul 2016
Conference number: 8
https://www.rmit.edu.au/events/all-events/conferences/2016/july/apira-2016

Conference

Conference8th Asia-Pacific Interdisciplinary Research in Accounting Conference (APIRA)
Abbreviated titleAPIRA
CountryAustralia
CityMelbourne
Period13/07/1615/07/16
Internet address

Fingerprint

Fraud detection
Financial statement fraud
Factors
Fraud
Selection process
Inclusion
Design methodology
Ad hoc
Variable selection

Cite this

Gepp, A. (2016). Addressing the problem of financial statement fraud: Better detection through improved models. Abstract from 8th Asia-Pacific Interdisciplinary Research in Accounting Conference (APIRA), Melbourne, Australia.
Gepp, Adrian. / Addressing the problem of financial statement fraud : Better detection through improved models. Abstract from 8th Asia-Pacific Interdisciplinary Research in Accounting Conference (APIRA), Melbourne, Australia.
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Gepp, A 2016, 'Addressing the problem of financial statement fraud: Better detection through improved models' 8th Asia-Pacific Interdisciplinary Research in Accounting Conference (APIRA), Melbourne, Australia, 13/07/16 - 15/07/16, .

Addressing the problem of financial statement fraud : Better detection through improved models. / Gepp, Adrian.

2016. Abstract from 8th Asia-Pacific Interdisciplinary Research in Accounting Conference (APIRA), Melbourne, Australia.

Research output: Contribution to conferenceAbstractResearchpeer-review

TY - CONF

T1 - Addressing the problem of financial statement fraud

T2 - Better detection through improved models

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N2 - Purpose: This paper aims to facilitate the development of more effective models to detect fraud. Financial statement fraud continues to be a costly problem to society and detection of it is difficult. Better aids, such as fraud detection models, are needed to improve detection. The selection of explanatory variables is crucial to developing such models, and yet, surprisingly, the selection process in prior financial statement fraud detection studies is not standardised. This paper proposes a new framework for this purpose. Design/methodology/approach: The new framework adapts the well-known Fraud Triangle, which was originally designed to explain drivers of fraudulent behaviour and needs to be adapted for use in fraud detection. The adaptation is done by the inclusion of a new Suspicious Information factor. This new framework also incorporates modifications suggested in prior research as extensions to the existing factors. Findings: Publicly available variables to operationalise each factor of the new Fraud Detection Triangle (FDT) framework already exist and there is preliminary empirical support for each factor in the FDT framework. Research into additional variables that measure R and S factors would be beneficial as less focus has been placed on them in prior research. Originality/Value/Practical Significance: The FDT is more suited to fraud detection than the previous fraud triangle. As a guiding theory for variable selection (previously done primarily on an ad hoc basis), it can facilitate the development of more accurate detection models. The variables analysed to operationalise the framework are also more comprehensive compared with prior research.

AB - Purpose: This paper aims to facilitate the development of more effective models to detect fraud. Financial statement fraud continues to be a costly problem to society and detection of it is difficult. Better aids, such as fraud detection models, are needed to improve detection. The selection of explanatory variables is crucial to developing such models, and yet, surprisingly, the selection process in prior financial statement fraud detection studies is not standardised. This paper proposes a new framework for this purpose. Design/methodology/approach: The new framework adapts the well-known Fraud Triangle, which was originally designed to explain drivers of fraudulent behaviour and needs to be adapted for use in fraud detection. The adaptation is done by the inclusion of a new Suspicious Information factor. This new framework also incorporates modifications suggested in prior research as extensions to the existing factors. Findings: Publicly available variables to operationalise each factor of the new Fraud Detection Triangle (FDT) framework already exist and there is preliminary empirical support for each factor in the FDT framework. Research into additional variables that measure R and S factors would be beneficial as less focus has been placed on them in prior research. Originality/Value/Practical Significance: The FDT is more suited to fraud detection than the previous fraud triangle. As a guiding theory for variable selection (previously done primarily on an ad hoc basis), it can facilitate the development of more accurate detection models. The variables analysed to operationalise the framework are also more comprehensive compared with prior research.

M3 - Abstract

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Gepp A. Addressing the problem of financial statement fraud: Better detection through improved models. 2016. Abstract from 8th Asia-Pacific Interdisciplinary Research in Accounting Conference (APIRA), Melbourne, Australia.