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.

Fraud is increasingly common, and so are the losses caused by this phenomenon. There is, thus, an essential economic incentive to study this problem, particularly fraud prevention. One barrier complicating the research in this direction is the lack of public data sets that embed fraudulent activities. In addition, although efforts have been made to detect fraud using machine learning, such actions have not considered the component of human behavior when detecting fraud. We propose a mechanism to detect potential fraud by analyzing human behavior within a data set in this work. This approach combines a predefined topic model and a supervised classifier to generate an alert from the possible fraud-related text. Potential fraud would be detected based on a model built from such a classifier. As a result of this work, a synthetic fraud-related data set is made. Four topics associated with the vertices of the fraud triangle theory are unveiled when assessing different topic modeling techniques. After benchmarking topic modeling techniques and supervised and deep learning classifiers, we find that LDA, random forest, and CNN have the best performance in this scenario. The results of our work suggest that our approach is feasible in practice since several such models obtain an average AUC higher than 0.8. Namely, the fraud triangle theory combined with topic modeling and linear classifiers could provide a promising framework for predictive fraud analysis.

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