Abstract
Objective: Financial fraud has been a big concern for many organizations across industries; billions of dollars are lost yearly because of this fraud. So businesses employ data mining techniques to address this continued and growing problem. This paper aims to review research studies conducted to detect financial fraud using data mining tools within one decade and communicate the current trends to academic scholars and industry practitioners. Method: Various combinations of keywords were used to identify the pertinent articles. The majority of the articles retrieved from Science Direct but the search spanned other online databases (e.g., Emerald, Elsevier, World Scientific, IEEE, and Routledge - Taylor and Francis Group). Our search yielded a sample of 65 relevant articles (58 peer-reviewed journal articles with 7 conference papers). One-fifth of the articles was found in Expert Systems with Applications (ESA) while about one-tenth found in Decision Support Systems (DSS). Results: 41 data mining techniques were used to detect fraud across different financial applications such as health insurance and credit card. Logistic regression model appeared to be the leading data mining tool in detecting financial fraud with a 13% of usage.In general, supervised learning tool have been used more frequently than the unsupervised ones. Financial statement fraud and bank fraud are the two largest financial applications being investigated in this area – about 63%, which corresponds to 41 articles out of the 65 reviewed articles. Also, the two primary journal outlets for this topic are ESA and DSS. Conclusion: This review provides a fast and easy-to-use source for both researchers and professionals, classifies financial fraud applications into a high-level and detailed-level framework, shows the most significant data mining techniques in this domain, and reveals the most countries exposed to financial fraud.
Highlights
Financial fraud has been a big concern for many organizations across industries and in different countries since it brings huge devastations to business
Financial fraud is normally discovered through outlier detection process [32] enabled by data mining techniques, which identify valuable information by revealing hidden trends, relationships, patterns found in a large database [25]
Most of the relevant articles were found in MIS related journals, e.g., Expert Systems with Applications and Decision Support Systems but some were found in finance and economic related journals, e.g., Journal of Risk and Insurance, and Applied
Summary
Financial fraud has been a big concern for many organizations across industries and in different countries since it brings huge devastations to business. Data mining, defined as “a process that uses statistical, mathematical, artificial intelligence, and machine learning techniques to extract and identify useful information and subsequently gain knowledge from a large database” [50], is a major contributor for detecting different types of financial fraud through its diverse methods, such as, logistic regression, decision tree, support vector machine (SVM), neural network (NN) and naïve Bayes. Some of these techniques outperform the others in specific financial contexts. This paper is an attempt to leverage our knowledge and to increase our understanding of data mining applications in financial fraud
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