Establishing Causality to Detect Fraud in Financial Statements

Abstract

In this work, two approaches have been presented to derive the important variables that an auditor should watch out for during the audit trials of a financial statement. To achieve this goal, machine learning modeling is leveraged. In the first approach, important features or variables are derived based on ensemble method and in the second approach, an explainable model is used to corroborate and expand the conclusions derived from the ensemble method. A dataset of financial statements that was labeled manually is utilized for this purpose. Four important measures, namely, random forest recommendations of first approach, random Forest Explaner -pvalue, random Forest Explainer-first multi-way importance plot and random Forest Explainer-second multi-way importance plot, are employed to derive the important features. A final list of six variables is derived from these two approaches and four measures

A model to detect financial statement fraud in portuguese companies by the auditor

“…While the costs of fraud could be reduced by increasing financial literacy, this depends on the will of governments (Engels, Kumar, and Philip, 2020). Thus, FS fraud detection is of extreme importance (Kopun, 2018;Maka, Pazhanirajan, and Mallapur, 2020). Nonetheless, the literature usually considers fraud to be less serious than other crimes, despite the substantially higher costs to society (Baird, Zelin II and Olson, 2016;Azmi, Yusoff and Ismail, 2020).…”