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While support vector machine is represented by a 9% and naïve Bayes is represented by a 6%

Detecting Financial Fraud Using Data Mining Techniques: A Decade Review from 2004 to 2015

Journal of data science, no. 3 (2016): 553-569

Cited by: 44|Views21
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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 t...More

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Introduction
  • Financial fraud has been a big concern for many organizations across industries and in different countries since it brings huge devastations to business.
  • 554 Detecting Financial Fraud Using Data Mining Techniques: A Decade Review from 2004 to 2015 fraud.
  • Detecting financial fraud is considered a high priority for many organizations, the current literature lacks for an up-to-date, comprehensive and in-depth review that can help firms with their decisions of selecting the appropriate data mining technique.
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]
  • Jans et al (2011) further classify internal fraud into two categories: financial statement fraud and transaction fraud [31]. They define financial statement fraud as “the intentional misstatement of certain financial values to enhance the appearance of profitability and deceive shareholders or creditors” while transaction fraud captures the process of snatching organizational assets
  • This section highlights the most frequent data mining techniques used in financial fraud associated with their usage frequency, description and business application
  • While support vector machine is represented by a 9% and naïve Bayes is represented by a 6%
  • Data mining techniques can address a wide array of business applications, for example, bankruptcy prediction, sales forecasting and scheduling optimization as shown in Table 4
Results
  • Ngai et al (2011) provide a well-organized and detailed literature review on detecting financial fraud via data mining methods based on 49 articles ranging from 1997 to 2008 [50].
  • 1,515 Taiwanese NN, logistic regression financial reporting firms (6 fraud, 1,509 and decision tree non-fraud): 2003-
  • 64 Credit card fraud 9,387 transactions from Turkish bank analysis, decision tree, (8,448 legitimate, NN and Naïve Bayes
  • This section highlights the most frequent data mining techniques used in financial fraud associated with their usage frequency, description and business application.
  • Logistic regression model appears to be the leading data mining technique in detecting financial fraud with a 13%, followed by both of neural network and decision tree, with a 11%.
  • Based on the analysis of the reviewed articles in this area, it is possible to classify financial fraud at a high-level into four major categories, namely, financial statement fraud, bank fraud, insurance fraud, and other related financial fraud (Table 5).
  • This framework can provide industry professionals an index to select the appropriate data mining technique for a specific context of financial fraud.
  • Firms that suffer from credit card fraud, they have an option of employing any of the supervised learning tools and it is recommended to go with the most frequent used technique; decision tree.
  • The 65 articles explored may not reveal the entire story of data mining usage in the domain of financial fraud; several online databases need to be included in the sample for more powerful presentation and analysis.
Conclusion
  • It shows the importance of the investigated data mining techniques in the domain of financial fraud by presenting their frequency, usage percentage, and other general business applications.
  • It is notable that logistic regression, decision tree, SVM, NN and Bayesian networks have been widely used (> 50%) to detect financial fraud, they are not always associated with the best classification results.
  • The highlighted aspects through this review can provide organizations with useful information regarding the various types of financial fraud and data mining techniques available to them.
Tables
  • Table1: Summarized work for detecting financial fraud via data mining techniques (2004-2015)
  • Table2: Distribution of articles by journals and conferences (2004–2015)
  • Table3: The number of articles for detecting financial fraud by countries
  • Table4: Most used data mining methods, their usage frequency, description and general business application
  • Table5: Classification of fraud types examined by data mining methods in one decade
  • Table6: Further break-down for fraud types with corresponding data mining techniques
Download tables as Excel
Funding
  • This paper emphasizes the huge increase of research conducted to address financial fraud in the years of 2008, 2009, 2011 and 2012. These four years account approximately for more than 50% of the publications in the 10-year period
Study subjects and analysis
relevant articles: 65
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). Onefifth of the articles was found in Expert Systems with Applications (ESA) while about one-tenth found in Decision Support Systems (DSS)

articles: 41
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

relevant articles: 65
Table 2 lists thirty-nine titles for both journals and conferences included in our analysis.

Although the majority of the articles retrieved from Science Direct, 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 while about one-tenth found in Decision Support Systems (Table 2)

articles: 49
Although detecting financial fraud is considered a high priority for many organizations, the current literature lacks for an up-to-date, comprehensive and in-depth review that can help firms with their decisions of selecting the appropriate data mining technique. Ngai et al (2011) provide a well-organized and detailed literature review on detecting financial fraud via data mining methods based on 49 articles ranging from 1997 to 2008 [50]. However, the specified time period is not able to capture the increasing trend of research in this area, specifically in the year of 2011, which is considered as a record year in financial fraud [11]

examined articles: 65
Literature has tapped on different types of financial fraud using different methods of data mining. Table 1 presents the 65 examined articles in chronological order. From the table, we can determine what methods are being frequently implemented for which case of financial fraud and what method can work best across fraud types

medical cases: 1812
SVM and nearest neighbour. 10 Health insurance 1812 medical cases. (906 fraud, 906 nonfraud )

observations: 60962
10,000 automobile fraud claims (9,899 legitimate, 101 fraudulent): 2000. 26 Healthcare fraud 60,962 observations Stepwise multi-stage from Medicaid clustering payment data. 148 firms (24 fraud, Classification and

relevant articles: 65
Although the majority of the articles retrieved from Science Direct, 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 while about one-tenth found in Decision Support Systems (Table 2)

articles: 41
The table shows the number of articles found in each type of financial fraud while the small pieces of pie chart represent those numbers in percentages. It is evident that financial statement fraud and bank fraud constitute the largest portion (63%) – this percentage corresponds to 41 articles out of the 65 reviewed articles. Fraud Type (application) Financial statement fraud

articles: 65
Y2004 Y2005 4% 8%. Table 7 and Chart 1 above highlight the yearly distribution of the 65 articles across the 10year period. The gray highlighted years (2008, 2009, 2010 and 211) account for more than a half of publications in financial fraud detection

articles: 65
Second, a decade review may not be sufficient to address this growing problem as it started when the business started. Third, the 65 articles explored may not reveal the entire story of data mining usage in the domain of financial fraud; several online databases need to be included in the sample for more powerful presentation and analysis. However, it is crucial to have a wide-ranging review on detecting financial fraud in order to increase the understanding and to expand the knowledge of this area among researchers and professionals

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Mousa Albashrawi
Mousa Albashrawi
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