CFP last date
22 July 2024
Reseach Article

Financial Frauds: Data Mining based Detection – A Comprehensive Survey

by Aastha Bhardwaj, Rajan Gupta
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 156 - Number 10
Year of Publication: 2016
Authors: Aastha Bhardwaj, Rajan Gupta

Aastha Bhardwaj, Rajan Gupta . Financial Frauds: Data Mining based Detection – A Comprehensive Survey. International Journal of Computer Applications. 156, 10 ( Dec 2016), 20-28. DOI=10.5120/ijca2016912538

@article{ 10.5120/ijca2016912538,
author = { Aastha Bhardwaj, Rajan Gupta },
title = { Financial Frauds: Data Mining based Detection – A Comprehensive Survey },
journal = { International Journal of Computer Applications },
issue_date = { Dec 2016 },
volume = { 156 },
number = { 10 },
month = { Dec },
year = { 2016 },
issn = { 0975-8887 },
pages = { 20-28 },
numpages = {9},
url = { },
doi = { 10.5120/ijca2016912538 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-07T00:02:14.921777+05:30
%A Aastha Bhardwaj
%A Rajan Gupta
%T Financial Frauds: Data Mining based Detection – A Comprehensive Survey
%J International Journal of Computer Applications
%@ 0975-8887
%V 156
%N 10
%P 20-28
%D 2016
%I Foundation of Computer Science (FCS), NY, USA

Financial fraud is a global problem and had affected the economy worldwide. Data mining being one of the most effective and powerful tool for detecting financial fraud had been used widely by the business analysts and researchers. This survey paper formalizes different types of financial frauds, summarizes the effective attributes for detecting each type of fraud, and present the latest developments on the use of data mining as a detection tool for financial frauds. The present survey analyses almost all published research work in the field of financial fraud detection for the period of 7 years starting from 2009. Its aim is to help researchers in identifying the suitable variables and data mining techniques by providing the landscape of research platforms for detection of financial fraud.

  1. Elkan, C. (2001).Magical Thinking in Data Mining: Lessons from COIL Challenge 2000. Proc. of SIGKDD01,426-431.
  2. Turban, E., Aronson, J.E., Liang, T.P., &Sharda, R. (2007).” Decision Support and Business Intelligence Systems”, Eighth edition, Pearson Education, 2007.
  3. Gupta and Nasib S. Gill (2012), “Prevention and Detection of Financial Statement Fraud – An Implementation of Data Mining Framework”, International Journal of Advanced Computer Science and Applications, Volume 3 No. 8, pp. 150 – 156, Published by The Science and Information Organization, U.S.A.
  4. Belinna Bai, Jerome yen, Xiaoguang Yang, False Financial Statements: Characteristics of China Listed Companies and CART Detection Approach, International Journal of Information Technology and Decision Making , Volume 7, No. 2(2008), pp. 339 – 359.
  5. M. Cecchini, H. Aytug, G.J. Koehler, and P. Pathak. “Detecting Management Fraud in Public Companies.”, Management Science, Volume 56, No. 10, 2010, pp. 1146 – 1160.
  6. Johan Perols, Financial Statement Fraud Detection: An Analysis of Statistical and Machine Learning Algorithms, A Journal of Practice &Theory, 30 (2), 19 (2011), pp. 19-50.
  7. P. Ravisankar, V. Ravi, G.Raghava Rao, I., Bose, Detection of Financial Statement Fraud and Feature Selection using Data Mining Techniques, Decision Support Systems, Volume 50 (2011), pp. 491 – 500.
  8. Fanning, K., &Cogger, K. (1998). Neural network detection of management fraud using published financial data. International Journal of Intelligent Systems in accounting, Finance & Management, 7(1), 21–24.
  9. E.I. Altman, Financial ratios, discriminant analysis and prediction of corporate bankruptcy, The Journal of Finance 23 (4) (1968) 589–609.
  10. Stice J., Albrecht S. and Brown L., (1991), ‘Lessons to be learned-ZZZZBEST, Regina, and Lincoln Savings’, The CPA Journal, April, pp. 52-53.
  11. Dalnial, Hawariah, et al. "Detecting Fraudulent Financial Reporting through Financial Statement Analysis." Journal of Advanced Management Science Vol 2.1 (2014).
  12. Spathis, C., M. Doumpos and C. Zopounidis. 2002. “Detecting falsified financial statements: a comparative study using multicriteria analysis and multivariate statistical techniques”. The European Accounting Review, 11 (3): 509-535.
  13. Kirkos, E., C. Spathis and Y. Manolopoulos. 2007. Data mining techniques for the detection of fraudulent financial statements. Expert Systems with Applications, 32 (4): 995-1003.
  14. H. Ali ATA, İbrahim H. SEYREK. "The use of data mining techniques in detecting fraudulent financial statements: an application on manufacturing firms.2009." The Journal of Faculty of Economics and Administrative Sciences Vol.14, No.2 pp.157-170.
  15. ATM & Card Statistics for May 2015 by RBI. Available at
  16. Report available at
  17. Report available at
  18. Edelstien, H.A. (1999). Introduction to data mining and knowledge discovery. (2nd Ed.), Two Crows Corporation.
  19. John Akhilomen.”Data Mining Application for Cyber Credit-card Fraud Detection System”; Journal of Engineering Science and Technology Vol. 6, No. 3 (2011) 311 - 322 . Proceedings of the World Congress on Engineering 2013 Vol III, WCE 2013, July 3 - 5, 2013, London, U.K
  20. Suvasini Panigrahi , Amlan Kundu , Shamik Sural , A.K. Majumdar.” Credit card fraud detection: A fusion approach using Dempster–Shafer theory and Bayesian learning”. Information Fusion 10 (2009) 354–363.
  21. Azeem Ush Shan Khan, Nadeem Akhtar and Mohammad Naved Qureshi “Real-Time Credit-Card Fraud Detection using Artificial Neural Network Tuned by Simulated Annealing Algorithm”. DOI: 02.ITC.2014.5.65 © Association of Computer Electronics and Electrical Engineers, 2014
  22. Avinash Ingole, Dr. R. C. Thool .”Credit Card Fraud Detection Using Hidden Markov Model and Its Performance”. International Journal of Advanced Research in Computer Science and Software Engineering. June 2013.
  23. Dr. Jyotindra N. Dharwa Dr. Ashok R. Patel. A Data Mining with Hybrid Approach Based Transaction Risk Score Generation Model (TRSGM) for Fraud Detection of Online Financial Transaction. International Journal of Computer Applications (0975 – 8887) Volume 16– No.1, February 2011.
  24. Andrea DAL POZZOLO, Olivier CAELENb, Yann-A¨el LE BORGNEa, Serge WATERSCHOOT, Gianluca BONTEMPI. “Learned lessons in credit card fraud detection from a practitioner perspective”, 2014.
  25. India Forensic research available at
  26. Terisa, R. "Improving the defense lines: the future of fraud detection in the insurance industry (with fraud risk models, text mining, and social networks)." SAS Global forum, Washington. 2010.
  27. E.W.T. Ngai, H. Yong, Y.H. Wong, C. Yijun and S. Xin, “The application of data mining techniques in financial fraud detection: A Classification Framework and an Academic Review of Literature”. Decision Support Systems, Volume 50, Issue 3, February 2011.
  28. Bhowmik, Rekha. "Detecting auto insurance fraud by data mining techniques." Journal of Emerging Trends in Computing and Information Sciences 2.4 (2011): 156-162.
  29. Jenn-Long Liu and Chien-Liang Chen .”Application of Evolutionary Data Mining Algorithms to Insurance Fraud Prediction”. Proceedings of 2012 4th International Conference on Machine Learning and Computing IPCSIT vol. 25 (2012) © (2012) IACSIT Press, Singapore.
  30. Šubelj, Lovro, Štefan Furlan, and Marko Bajec. "An expert system for detecting automobile insurance fraud using social network analysis." Expert Systems with Applications 38.1 (2011): 1039-1052.
  31. Pradnya P. Sondwale.”Overview of Predictive and Descriptive Data Mining Technique”. International Journal of Advanced Research in Computer Science and Software Engineering, Volume 5, Issue 4, April 2015.
  32. Meenakshi, Sandeep Jaglan.” A Hybrid Data Mining Metaheuristic Approach for Anomaly Detection”. International Journal of Advanced Research in Computer Science and Software Engineering,2013.
  33. Comparative Analysis of Data Mining Techniques on Educational Dataset. Sumit Garg,Arvind K.Sharma. International Journal of Computer Applications, Volume 74– No.5, July 2013.
  34. FRANCISCA NONYELUM OGWUELEKA.”Data mining application in credit card fraud detection system” . Journal of Engineering Science and Technology Vol. 6, No. 3 ,2011.
  35. Sen, Sanjay Kumar, and Sujata Dash. "Meta Learning Algorithms for Credit Card Fraud Detection." Meta 6.6 (2013): 16-20.
  36. Zareapoor, Masoumeh, and Pourya Shamsolmoali. "Application of Credit Card Fraud Detection: Based on Bagging Ensemble Classifier." Procedia Computer Science, Pages 679-685(2015).
  37. Wu, Junjie, Hui Xiong, and Jian Chen. "COG: local decomposition for rare class analysis." Data Mining and Knowledge Discovery 20.2 (2010): 191-220.
  38. R.Devaki V.Kathiresan S.Gunasekaran,”Credit Card Fraud Detection using Time Series Analysis”. International Journal of Computer Applications, 2014.
  39. Rodrigues, Luis Alexandre, and Nizam Omar. "Auto claim fraud detection using multi classifier system." Journal of Computer Science & Information Technology (2014).
  40. Chen, Suduan. "Detection of fraudulent financial statements using the hybrid data mining approach." SpringerPlus 5.1 (2016)
Index Terms

Computer Science
Information Sciences


Financial fraud Management fraud Customer fraud Task relevant data Data mining Credit card fraud Insurance fraud.