Contributions and Limitations of Statistical Methods Used to Identify Financial Statement Fraud
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Beschreibung
This article examines the effectiveness of statistical methods in identifying companies that engage in financial statement fraud (FSF) and describes the variables included in the tests and the level of analysis. The study uses an extensive literature review to compare methods like Discriminant Analysis (DA), Logistic Regression (LR) and Multicriteria Decision Aid (MCDA) in terms of their characteristics and the results obtained. Previous studies pursue the analysis solely at the company-level, overseeing the characteristics of the fraud perpetrators. The literature review conducted establishes the need for further research in order to extend the level of the analysis. This article proposes an approach that includes the individual-level factors determining fraud, based on a typology of employees’ characteristics and intrinsic motivation drivers that might influence their choice to engage in illegal practices. In this way, a study that makes use of the synergy between individual and aggregate level data could provide a clearer overview of the cause-and-effect chain of opportunities, pressures, and attitudes that determine FSF.
This article examines the effectiveness of statistical methods in identifying companies that engage in financial statement fraud (FSF) and describes the variables included in the tests and the level of analysis. The study uses an extensive literature review to compare methods like Discriminant Analysis (DA), Logistic Regression (LR) and Multicriteria Decision Aid (MCDA) in terms of their characteristics and the results obtained. Previous studies pursue the analysis solely at the company-level, overseeing the characteristics of the fraud perpetrators. The literature review conducted establishes the need for further research in order to extend the level of the analysis. This article proposes an approach that includes the individual-level factors determining fraud, based on a typology of employees’ characteristics and intrinsic motivation drivers that might influence their choice to engage in illegal practices. In this way, a study that makes use of the synergy between individual and aggregate level data could provide a clearer overview of the cause-and-effect chain of opportunities, pressures, and attitudes that determine FSF.
Autor*in
Maria-Magdalena Stancioi