A Review of Data Mining-Based Financial Fraud Detection Research

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How researchers see this paper

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Intelligent financial fraud detection: A comprehensive review (2016, unknown): “They claimed that the only successful methods of fraud detection to date, as well as the most commonly researched, were classification based [55].”
MetaFraud: A Meta-Learning Framework for Detecting Financial Fraud (2012, background): “Most prior studies of financial fraud used only information based on annual statements (Yue et al. 2007).”
A Review of Financial Accounting Fraud Detection based on Data Mining Techniques (2012, unknown): “The accounting fraud is executed by making falsified financial accounting statements where the numbers are manipulated by overstating assets, spurious entries related to sales and profit, misappropriation in taxes, or understating liabilities, debts, expenses or losses [1].”
A Semi-Supervised Graph Attentive Network for Financial Fraud Detection (2019, methodology): “These methods make prediction mainly based on the statistical features of a certain user and use classical classiﬁers like logistic regression, neural networks to do classiﬁcation [2]–[4].”