Abstract:Occupational fraud is defined as the deliberate misuse of one's occupation for personal enrichment. It poses a significant challenge for organizations and governments. Estimates indicate that the funds involved in occupational fraud cases investigated across 125 countries between 2018 and 2019 exceeded US$3.6 billion. Process-based fraud (PBF) is a form of occupational fraud that is perpetrated inside business processes. Business processes underlie the logic of the work that organizations undertake, and they a… Show more
“…However, even when the data are available, large and small datasets should be addressed differently [11]. In any case, as a human behavior, fraud detection is a multidimensional problem, and so are some of the fraud-detection mechanisms proposed in the literature [12,13].…”
Fraud entails deception in order to obtain illegal gains; thus, it is mainly evidenced within financial institutions and is a matter of general interest. The problem is particularly complex, since perpetrators of fraud could belong to any position, from top managers to payroll employees. Fraud detection has traditionally been performed by auditors, who mainly employ manual techniques. These could take too long to process fraud-related evidence. Data mining, machine learning, and, as of recently, deep learning strategies are being used to automate this type of processing. Many related techniques have been developed to analyze, detect, and prevent fraud-related behavior, with the fraud triangle associated with the classic auditing model being one of the most important of these. This work aims to review current work related to fraud detection that uses the fraud triangle in addition to machine learning and deep learning techniques. We used the Kitchenham methodology to analyze the research works related to fraud detection from the last decade. This review provides evidence that fraud is an area of active investigation. Several works related to fraud detection using machine learning techniques were identified without the evidence that they incorporated the fraud triangle as a method for more efficient analysis.
“…However, even when the data are available, large and small datasets should be addressed differently [11]. In any case, as a human behavior, fraud detection is a multidimensional problem, and so are some of the fraud-detection mechanisms proposed in the literature [12,13].…”
Fraud entails deception in order to obtain illegal gains; thus, it is mainly evidenced within financial institutions and is a matter of general interest. The problem is particularly complex, since perpetrators of fraud could belong to any position, from top managers to payroll employees. Fraud detection has traditionally been performed by auditors, who mainly employ manual techniques. These could take too long to process fraud-related evidence. Data mining, machine learning, and, as of recently, deep learning strategies are being used to automate this type of processing. Many related techniques have been developed to analyze, detect, and prevent fraud-related behavior, with the fraud triangle associated with the classic auditing model being one of the most important of these. This work aims to review current work related to fraud detection that uses the fraud triangle in addition to machine learning and deep learning techniques. We used the Kitchenham methodology to analyze the research works related to fraud detection from the last decade. This review provides evidence that fraud is an area of active investigation. Several works related to fraud detection using machine learning techniques were identified without the evidence that they incorporated the fraud triangle as a method for more efficient analysis.
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