It comes as no surprise to learn that from an economic standpoint, fraud continues to be a growing concern for organisations of all sizes, across all regions and in virtually every sector. A 2014 survey shows that 5% of the losses at an organization can be attributed to fraud, which applied to the Gross World Producttranslates to a projected global fraud revenue loss of nearly $3.7 trillion. [1] Due to ever increasing volume of data that needs to be analysed in order to detect these frauds, data mining methods and techniques are being used with increasing frequency in this domain. This paper is aimed at providing an expansive literature review of journal articles produced between 2008 and 2015 to demonstrate the extensive research that has been carried out in selected domains and also to highlight the gaps between industry need and research in the particular areas. We have classified the research papers based on the data mining technique used, the type of fraud targeted, year of publishing, etc. and analysed the results.

Data Mining; Fraud; Fraud Detection; Classification; Support Vector Machine; Computer Intrusion

[1] "Report to the Nations on Occupational Fraud and Abuse: 2014 Global Study," 2014. [Online]. Available: http://www.acfe.com/rttn/docs/2014-report-to-nations.pdf.
[2] B. Palace, What is data mining?, Anderson Graduate School of Management at UCLA, 1996.
[3] A. W.-C. F. Raymond Chi-Wing Wong, Association Rule Mining and its Application to MPIS.
[4] G. P.-S. a. P. S. Usama Fayyad, "From Data Mining to Knowledge Discovery in Databases," American Association for Artificial Intelligence, 1996.
[5] D. A. Keim, "Information Visualization and Data Mining," IEEE Transactions on Visualization and Computer Graphics, 2002.
[6] "FindLaw," [Online]. Available: http://criminal.findlaw.com/criminal-charges/fraud-financial-crimes.html.
[7] G. C. Y. B. N. Grozavu, "Unsupervised Learning for Analyzing the Dynamic Behavior of Online Banking Fraud," International Conference on Data Mining Workshops IEEE, 2013.
[8] D. C. Y. ,. W.-H. L. ,. C. W. Shing-Han Li, "Identifying the signs of fraudulent accounts using data mining techniques," Computers in Human Behavior Elsevier, 2012.
[9] Q. Deng, "Application of Support Vector Machine in the Detection of Fraudulent Financial Statements," 4th International Conference on Computer Science & Education, 2009.
[10] Q. Deng, "Detection of Fraudulent Financial Statements Based on Naïve Bayes Classifier," The 5th International Conference on Computer Science & Education, 2010.
[11] O. R. Z. Koosha Golmohammadi, "Data Mining Applications for Fraud Detection in Securities Market," European Intelligence and Security Informatics Conference IEEE, 2012.
[12] A. A. S. A. S. Evmorfia N. Argyriou, "Occupational Fraud Detection Through Visualization," IEEE, 2013.
[13] H. R. S. Shahla Mardani, "A New Method for Occupational Fraud Detection in Process Aware Information Systems," International ISC Conference on Information Security and Cryptology IEEE, 2013.
[14] J. D. V. Pamela Castellón González, "Characterization and detection of taxpayers with false invoices using data mining techniques," Expert Systems with Applications- Elsevier, 2013.
[15] L. D. V. K. N. E. N. T. E. K. Zhiyuan Chen, "Exploration of the Effectiveness of Expectation Maximization Algorithm for Suspicious Transaction Detection in Anti-Money Laundering," IEEE International Conference on Open Systems, 2014.
[16] Q. Deng, "Detection of Fraudulent Financial Statements," IEEE - Granular Computing 2009, 2009.
[17] O. O. ,. O. O. Abiodun. Modupe Oludayo, "Exploring Support Vector Machines and Random Forests to Detect Advanced Fee Fraud Activities on Internet," IEEE International Conference on Data Mining Workshops, 2011.
[18] "CULS, Cornell University Law School, White-Collar Crime: an overview," [Online]. Available: http://topics.law.cornell.edu/wex/White-collar_crime (2009).
[19] W. •. J. L. •. L. C. •. Y. O. J. Chen, "Effective detection of sophisticated online banking fraud on extremely imbalanced data," Springer Science+Business Media, 2012.
[20] "Cornell University Law School : Legal Information Institute," [Online]. Available: https://www.law.cornell.edu/wex/credit_card_fraud.
[21] L. A. V. D. A. M. d. C. F. S. M. Emanuel Mineda Carneiro, "Cluster Analysis and Artificial Neural Networks : A Case Study in Credit Card Fraud Detection," International Conference on Information Technology - New Generations IEEE, 2015.
[22] Report of the National Commission on Fraudulent Financial Reporting, New York: American Institute of Certified Public Accountants (AICPA), National Commission on Fraudulent Financial Reporting (Treadway Commission), 1987.
[23] V. R. ,. G. R. R. ,. I. B. P. Ravisankar, "Detection of financial statement fraud and feature selection using data mining techniques," Decision Support Systems- Elsevier, 2011.
[24] W. X. T. Xinyang Li, "How to Protect Investors? A GA-based DWD Approach for Financial Statement Fraud Detection," IEEE International Conference on Systems, Man, and Cybernetics, 2014.
[25] R. M. Manoj Kumar, "Unsupervised Outlier Detection Technique for Intrusion Detection in Cloud Computing," International Conference for Convergence of Technology IEEE, 2014.
[26] L. B. T. F. M. B. Mohammad Behdad, "On XCSR for electronic fraud detection," Springer-Verlag , 2012.
[27] L. Z. a. S. X. Han Tao, "Insurance Fraud Identification Research Based on Fuzzy Support Vector Machine with Dual Membership," International Conference on Information Management, Innovation Management and Industrial Engineering, 2012.
[28] C. A. Melih Kirlidog, "A fraud detection approach with data mining in health insurance," Procedia - Social and Behavioral Sciences, 2012.
[29] J. Ariganello, What the Fraud? A Look at Telecommunications Fraud and its Impact.
[30] K. T. J. V. T. Anna Leontjeva, "Fraud Detection: Methods of Analysis for Hypergraph Data," IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 2012.
[31] G. P. T. D. P. A. Theodoros Kapourniotis, "Scam and Fraud Detection in VoIP Networks: Analysis and Countermeasures Using User Profiling," IEEE, 2011.
[32] M. M. S. Hamid Farvaresh, "A datamining framework for detecting subscription fraud in telecommunication," Engineering Applications of Artificial Intelligence.
[33] S. P. Sharmila Subudhia, "Quarter-Sphere Support Vector Machine for Fraud Detection in Mobile Telecommunication Networks," Procedia Computer Science, 2015.
[34] G. A. Vipul Rawte, "Fraud Detection in Health Insurance using Data Mining Techniques," International Conference on Communication, Information & Computing Technology (ICCICT), 2015.
[35] J. P. M. A. E. G. F. V. Ll. Bermúdeza, "A Bayesian dichotomous model with asymmetric link for fraud in insurance," Insurance: Mathematics and Economics Elsevier, 2008.
[36] K. S. Y. S. K. T. S. K. A. a. M. M. Jawad Nagi, "Nontechnical Loss Detection for Metered Customers in Power Utility Using Support Vector Machines," IEEE Transactions on Power Delivery Vol. 25, 2010.
[37] A. M. M. K. S. Y. S. K. T. J. Nagi, "Non-Technical Loss Analysis for Detection of Electricity Theft using Support Vector Machines," IEEE International Conference on Power and Energy, 2008.
[38] C. S. Hilas, "An application of supervised and unsupervised learning approaches to telecommunications fraud detection," Elsevier, 2008.
[39] Z. Y. D. P. Z. Anisah H Nizar, "Detection Rules for Non Technical Losses," IEEE, 2008.
[40] J. O. P. P. A. M. A. C. P. José E. Cabral, "Fraud Detection System for High and Low Voltage Electricity Consumers Based on Data Mining," Power & Energy Society General Meeting, 2009., 2009.
[41] C. S. Hilas, "An application of supervised and unsupervised learning approaches to telecommunications fraud detection," Elsevier, 2008.
[42] C. S. Hilas, "Designing an expert system for fraud detection in private telecommunications networks," Expert Systems with Applications Elsevier, 2009.
[43] D. Olszewski, "Fraud detection using self-organizing map visualizing the user profiles," Elsevier, 2014.
[44] H. T. a. A. N. R. Huang, "A Novel Hybrid Artificial Immune Inspired Approach for Online Break-in Fraud Detection," International Conference on Computational Science, IEEE, 2010.
[45] C. W. •. D. J. H. •. P. J. •. D. W. •. N. M. Adams, "Transaction aggregation as a strategy for credit card fraud detection," Springer Science+Business Media, 2008.
[46] M. I. E. D. T. C. M. Hamdi Ozelik, "Improving a credit card fraud detection system using genetic algorithm," International Conference on Networking and Information Technology, 2010.
[47] A. N. Tatsuya Minegishi, "Detection of Fraud Use of Credit Card by Extended VFDT," IEEE, 2011.
[48] L. Lei, "Card Fraud Detection by Inductive Learning and Evolutionary Algorithm," International Conference on Genetic and Evolutionary Computing IEEE, 2012.
[49] R. N. Sherly K.K, "BOAT Adaptive Credit Card Fraud Detection System," IEEE, 2010.
[50] R. D. Mukesh Kumar Mishra, "A Comparative Study of Chebyshev Functional Link Artificial Neural Network, Multi-Layer Perceptron and Decision Tree for Credit Card Fraud Detection," 13th International Conference on Information Technology IEEE, 2014.
[51] M. R. H. Leila Seyedhossein, "Mining Information from Credit Card Time Series for Timelier Fraud Detection," 5th International Symposium on Telecommunications, 2010.
[52] G. S. A. S. L. V. Addisson Salazar, "Automatic Credit Card Fraud Detection based on Non-linear Signal Processing," IEEE, 2012.
[53] N. W. Wen-Fang YU, "Research on Credit Card Fraud Detection Model Based on Distance Sum," International Joint Conference on Artificial Intelligence, 2009.
[54] N. W. Chun-Hua JU, "Research on Credit Card Fraud Detection Model Based on Similar Coefficient Sum," First International Workshop on Database Technology and Applications, 2009.
[55] P. S. Masoumeh Zareapoor, "Applications of Credit Card Fraud Detection: Based on Bagging Ensemble Classifier," Procedia Journal, 2015.
[56] Y. Z. ,. X. Timothy Matti, "Financial Fraud Detection Using Social Media Crowdsourcing," IEEE, 2014.
[57] M. R. H. Soheil Jamshidi, "An Efficient Data Enrichment Scheme for Fraud Detection Using Social Network Analysis," IEEE, 2012.
[58] G. M. Qingshan Deng, "Combining Self-Organizing Map and K-Means Clustering for Detecting Fraudulent Financial Statements," IEEE - Granular Computing 2009, 2009.