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Detecting Management Fraud in Public Companies

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Accusing the wrong companies of fraud is costly, both to the accused companies and to the public as a whole as such false claims to an injustice to law enforcement, investor and even public participation processes. Detecting Management Fraud in Public Companies is a refinement and confirmation study of the past studies that tested various models for detecting fraud, and updates them by checking how much more efficient they might be with newer methodologies that allow for more credible data management. By definition, some of the people who undertake fraudulent activities use tactics to hide their practices. The proposed revisions demonstrate that more sensitive techniques allow for at least better identifying the "grey area" where such companies may hide using publicly available data so as to minimize false presumptions to begin with (1158). Such refinements are seen as having specific, measurable returns for those seeking to eliminate fraud as well as for those who try to do honest assessments of the strength of companies who appear to be moving forward.
The authors opted to make their refinements in two ways. One was to assess the component approaches and assumptions of previous study approaches and then to replicate what was done in those studies using a second refinement of a new financial kernel that was proposed to be more amendable to publicly available knowledge. The goal of their efforts was to generate the best predictive tool against audit fraud while controlling

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