Call for Paper - January 2023 Edition
IJCA solicits original research papers for the January 2023 Edition. Last date of manuscript submission is December 20, 2022. Read More

A Data Mining Framework for Prevention and Detection of Financial Statement Fraud

International Journal of Computer Applications
© 2012 by IJCA Journal
Volume 50 - Number 8
Year of Publication: 2012
Rajan Gupta
Nasib Singh Gill

Rajan Gupta and Nasib Singh Gill and. Article: A Data Mining Framework for Prevention and Detection of Financial Statement Fraud. International Journal of Computer Applications 50(8):7-14, July 2012. Full text available. BibTeX

	author = {Rajan Gupta and Nasib Singh Gill and},
	title = {Article: A Data Mining Framework for Prevention and Detection of Financial Statement Fraud},
	journal = {International Journal of Computer Applications},
	year = {2012},
	volume = {50},
	number = {8},
	pages = {7-14},
	month = {July},
	note = {Full text available}


Financial statement fraud has reached the epidemic proportion globally. Recently, financial statement fraud has dominated the corporate news causing debacle at number of companies worldwide. In the wake of failure of many organisations, there is a dire need of prevention and detection of financial statement fraud. Prevention of financial statement fraud is a measure to stop its occurrence initially whereas detection means the identification of such fraud as soon as possible. Fraud detection is required only if prevention has failed. Therefore, a continuous fraud detection mechanism should be in place because management may be unaware about the failure of prevention mechanism. In this paper we propose a data mining framework for prevention and detection of financial statement fraud.


  • Atkins Matt, Accounting Fraud in US listed Chinese companies (September 2011). Available at: http://www. financierworldwide. com
  • Floyd Advisory LLC, Summary of Accounting and auditing enforcement releases for three months ended March 31, 2012.
  • ACFE, 2012 ACFE Report to the nations on ocupational fraud and abuse, Technical report- Global fraud survey 2012, 2012.
  • Bologna G. And Lindquist R. Fraud Auditing and Forensic Accounting. John Wiley & Sons, 1995
  • Chui, L. , and B. Pike. 2011. Auditors' responsibility for fraud detection: New wine in old bottles? In Proceedings of the American Accounting Association 2011 Annual Meeting. Denver.
  • Kantardzi c M. (2002), Data Mining: Concepts, Models, Methods, and Algorithms', Wiley – IEEE Press.
  • Beasley, M. (1996). An empirical analysis of the relation between board of director composition and financial statement fraud. The Accounting Review, 71(4), 443–466.
  • Green, B. P. , & Choi, J. H. (1997). Assessing the risk of management fraud through neural-network technology. Auditing: A Journal of Practice and Theory, 16(1), 14–28.
  • 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.
  • Efstathios Kirkos, Charalambos Spathis & Yannis Manolopoulos (2007). Data mining techniques for the detection of fraudulent financial statements. Expert Systems with Applications 32 (23) (2007) 995–1003
  • C. Spathis, M. Doumpos, C. Zopounidis, Detecting falsified financial statements: a comparative study using multicriteria analysis and multivariate statistical techniques, European Accounting Review 11 (3) (2002) 509–535.
  • M. Cecchini, H. Aytug, G. J. Koehler, and P. Pathak. Detecting Management Fraud in Public Companies. http://warrington. ufl. edu/isom/docs/papers/DetectingManagementFraudInPublicCompanies. pdf
  • S. -M. Huang, D. C. Yen, L. -W. Yang, J. -S. Hua, An investigation of Zipf's Law for fraud detection, Decision Support Systems 46 (1) (2008) 70–83.
  • Hoogs Bethany, Thomas Kiehl, Christina Lacomb and Deniz Senturk (2007). A Genetic Algorithm Approach to Detecting Temporal Patterns Indicative Of Financial Statement Fraud, Intelligent systems in accounting finance and management 2007; 15: 41 – 56, John Wiley & Sons, USA, available at: www. interscience. wiley. com
  • M. J. Cerullo, V. Cerullo, Using neural networks to predict financial reporting fraud: Part 1, Computer Fraud & Security 5 (1999) 14–17.
  • E. Koskivaara, Artificial neural networks in auditing: state of the art, The ICFAI Journal of Audit Practice 1 (4) (2004) 12–33.
  • B. Busta, R. Weinberg, Using Benford's law and neural networks as a review procedure, Managerial Auditing Journal 13 (6) (1998) 356–366.
  • H. C. Koh, C. K. Low, Going concern prediction using data mining techniques, Managerial Auditing Journal 19 (3) (2004) 462–476.
  • 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 , Vol. 7, No. 2(2008), 339 - 359
  • Juszczak, P. , Adams, N. M. , Hand, D. J. , Whitrow, C. , & Weston, D. J. (2008). Off-the-peg and bespoke classifiers for fraud detection?, Computational Statistics and Data Analysis, vol. 52 (9): 4521-4532
  • A. Deshmukh, L. Talluru, A rule-based fuzzy reasoning system for assessing the risk of management fraud, International Journal of Intelligent Systems in Accounting, Finance & Management 7 (4) (1998) 223–241.
  • Wei Zhou, G. Kappor, Detecting evolutionary financial statement fraud, Decision Support Systems 50 (2011) 570 – 575.
  • P. Ravisankar, V. Ravi, G. Raghava Rao, I. , Bose, Detection of financial statement fraud and feature selection using data mining techniques, Decision Support Systems, 50(2011) 491 - 500
  • 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
  • Mieke Jans, Nadine Lybaert & Koen Vanhoof. A Framework for Internal Fraud Risk reduction, an IT integrating business process: The IFR2 framework. The international Journal of Digital Accounting Research, 9, 1 – 29.
  • Ngai, E. W. T. , Hu, Y. , Wong, Y. H. , Chen, Y. , & Sun, X. (2010). The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature, Decision Support System (2010), doi:10. 1016/j. dss. 2010. 08. 006
  • Anuj Sharma & Prabin Kumar Panigrahi, " A review of financial Accounting Fraud Detection based on Data Mining Techniques", International Journal of Computer Application, Vol. 39 – No. 1, Feb. 2012 pp. 37 – 47
  • "Fraud, Waste, and Abuse – Prevention, Detection and Reporting for Federal, State, Local, and Tribal Administrators" United States Environmental Protection Agency (Office of Inspector General), www. epa. gov/oig
  • Rezaee Z. , Riley R. 2010. Financial statement fraud: prevention and detection. 2nd Edition New York: John Wiley.
  • ACFE, Tone at the top: How Management can prevent fraud in workplace, Available at: http://www. acfe. com/uploadedFiles/ACFE_Website/Content/documents/tone-at-the-top-research. pdf
  • "Report of the National Commission on Fraudulent Financial Reporting", Retrieved June 07, 2012 from http://www. coso. org/Publications/NCFFR. pdf
  • Fanning, K. , Cogger, K. , & Srivastava, R. (1995). Detection of management fraud: a neural network approach. International Journal of Intelligent Systems in Accounting, Finance & Management, vol. 4, no. 2, pp. 113– 26, June 1995.
  • Elkan, C. (2001). Magical Thinking in Data Mining: Lessons from COIL Challenge 2000. Proc. of SIGKDD01, 426-431
  • Turban, E. , Aronson, J. E. , Liang, T. P. , & Sharda, R. (2007). Decision Support and Business Intelligence Systems, Eighth edition, Pearson Education, 2007
  • Wang, S. (2010). A Comprehensive Survey of Data Mining-Based Accounting-Fraud Detection Research. International Conference on Intelligent Computation Technology and Automation, vol. 1, pp. 50-53, 2010.
  • Chien C. -F. and Chen L. -F. . Data mining to improve personnel selection and enhance human capital: A case study in high-technology industry. Expert Systems with Applications Vol 34, Issue I, Jan 2008 pp. 280-290.
  • Han Jiawei & Kamber Micheline, Data Mining: Concepts and Techniques, Morgan Koufmann Publishers
  • Cressey, D. R. 1986. Why managers commit fraud. Australian and New Zealand Journal of Criminology. 19(4): 195-209.
  • Albrecht W. , Albrecht C. , and Albrecht C. Fraud and corporate executives: Agency, stewardship and broken trust. Journal of Forensic Accounting 1524 – 5586 / vol. V (2004) pp. 109 – 130, R. T. Edwards Inc. , Printed in USA
  • Z. Rezaee, Financial Statement Fraud-Prevention and Detection, John Wiley &Sons, Inc. , 2002.