Abstract

Financial statement fraud has been a serious concern for many investors and other stakeholders, and this motivated many researchers in developing traditional regression analysis in order to detect financial statement fraud. Intelligent financial statements fraud detection systems were continuously developed to uncover any potential for fraud signals which could assist the stakeholders in taking their decisions. This study reviews and identifies the gaps in the previous studies that tested financial statement fraud detection using data mining techniques. The study expands the knowledge of this research topic among researchers, forensic accountants, auditors and regulators and provides organizations with useful information regarding the various types of financial fraud and data mining techniques available.

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