Identification of Financial Statement Fraud in Greece by Using Computational Intelligence Techniques

  • Christianna Chimonaki
  • Stelios Papadakis
  • Konstantinos Vergos
  • Azar ShahgholianEmail author
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 345)


The consequences of financial fraud are an issue with far-reaching for investors, lenders, regulators, corporate sectors and consumers. The range of development of new technologies such as cloud and mobile computing in recent years has compounded the problem. Manual detection which is a traditional method is not only inaccurate, expensive and time-consuming but also they are impractical for the management of big data. Auditors, financial institutions and regulators have tried to automated processes using statistical and computational methods. This paper presents comprehensive research in financial statement fraud detection by using machine learning techniques with a particular focus on computational intelligence (CI) techniques. We have collected a sample of 2469 observations since 2002 to 2015. Research gap was identified as none of the existing researchers address the association between financial statement fraud and CI-based detection algorithms and their performance, as reported in the literature. Also, the innovation of this research is that the selection of data sample is aimed to create models which will be capable of detecting the falsification in financial statements.


Financial statement fraud Machine learning techniques Classification 


  1. Association of Certified Fraud Examiners’ (ACFE’s) in its report to the nation on occupational fraud and abuse (2014)Google Scholar
  2. Bartley, J.W., Boardman, C.M.: The relevance of inflation adjusted accounting data to the prediction of corporate takeovers. J. Bus. Financ. Acc. 17(1), 53–72 (1990)CrossRefGoogle Scholar
  3. Beaver, W.H.: Financial ratios as predictors of failure. J. Acc. Res. 4, 71–111 (1966)CrossRefGoogle Scholar
  4. Bose, I., Wang, J.: Data mining for detection of financial statement fraud in Chinese Companies. In: Paper Presented at the International Conference on Electronic Commerce, Administration, Society and Education, Hong Kong, pp. 15–17, August 2007Google Scholar
  5. Cosslett, S.R.: Efficient estimation of discrete-choice models. In: Structural Analysis of Discrete Data with Econometric Applications (1981)Google Scholar
  6. Fanning, K.M., Cogger, K.O.: Neural network detection of management fraud using published financial data. Int. J. Intell. Syst. Acc. Financ. Manag. 7(1), 21–41 (1998)CrossRefGoogle Scholar
  7. Feroz, E.H., Park, K., Pastena, V.S.: The financial and market effects of the SEC’s accounting and auditing enforcement releases. J. Acc. Res. 29, 107–142 (1991)CrossRefGoogle Scholar
  8. Humpherys, S.L., Moffitt, K.C., Burns, M.B., Burgoon, J.K., Felix, W.F.: Identification of fraudulent financial statements using linguistic credibility analysis. Decis. Support Syst. 50, 585–594 (2011)CrossRefGoogle Scholar
  9. Hunt, H.G., Ord, J.K.: Matched pairs discrimination: methodology and an investigation of corporate accounting policies. Decis. Sci. 19(2), 373–382 (1988)CrossRefGoogle Scholar
  10. Kinney, W.R., McDaniel, L.S.: Characteristics of firms correcting previously reported quarterly earnings. J. Acc. Econ. 11(1), 71–93 (1989)CrossRefGoogle Scholar
  11. Kirkos, E., Spathis, C., Manolopoulos, Y.: Data mining techniques for the detection of fraudulent financial statements. Expert Syst. Appl. 32(4), 995–1003 (2007)CrossRefGoogle Scholar
  12. Kreutzfeldt, R., Wallace, W.: Error characteristics in audit populations: their profile and relationship to environment factors. Auditing J. Pract. Theory 6, 20–43 (1986)Google Scholar
  13. Loebbecke, J.K., Eining, M.M., Willingham, J.J.: Auditors experience with material irregularities-frequency, nature, and detectability. Audit. J. Pract. Theory 9(1), 1–28 (1989)Google Scholar
  14. Maes, S., Tuyls, K., Vanschoenwinkel, B., Manderick, B.: Credit card fraud detection using Bayesian and neural networks. In: Proceedings of the 1st International Naiso Congress on Neuro Fuzzy Technologies (2002)Google Scholar
  15. Ngai, E., Hu, Y., Wong, Y., Chen, Y., Sun, X.: The application of data mining techniques in financial fraud detection: a classification framework and an academic review of literature. Decis. Support Syst. 50, 559–569 (2011)CrossRefGoogle Scholar
  16. Ohlson, J.A.: Financial ratios and the probabilistic prediction of bankruptcy. J. Acc. Res. 18, 109–131 (1980)CrossRefGoogle Scholar
  17. Palepu, K.G.: Predicting takeover targets: a methodological and empirical analysis. J. Account. Econ. 8(1), 3–35 (1986)CrossRefGoogle Scholar
  18. Parsons, S.: Current approaches to handling imperfect information in data and knowledge bases. IEEE Trans. Knowl. Data Eng. 8(3), 353–372 (1996)MathSciNetCrossRefGoogle Scholar
  19. Persons, O.S.: Using financial statement data to identify factors associated with fraudulent financial reporting. J. Appl. Bus. Res. 11(3), 38 (1995)CrossRefGoogle Scholar
  20. Ravisankar, P., Ravi, V., Rao, G.R., Bose, I.: Detection of financial statement fraud and feature selection using data mining techniques. Decis. Support Syst. 50(2), 491–500 (2011)CrossRefGoogle Scholar
  21. Ren, J., Lee, S.D., Chen, X., Kao, B., Cheng, R., Cheung, D.: Naive bayes classification of uncertain data. In: 2009 Ninth IEEE International Conference on Data Mining, 2009, pp. 944–949 (2009)Google Scholar
  22. Rezaee, Z.: In Financial statement fraud: prevention and detection. Wiley, Hoboken (2002)Google Scholar
  23. Sibley, A., Burch, E.E.: Optimal selection of matched pairs from large data bases. Decis. Sci. 10(1), 62–70 (1979)CrossRefGoogle Scholar
  24. Spathis, C., et al.: Detecting false financial statements using published data: some evidence from Greece. Manag. Auditing J. 17(4), 179-191 (2002a)CrossRefGoogle Scholar
  25. Spathis, C., Doumpos, M., Zopounidis, C.: Detecting falsified financial statements: a comparative study using multicriteria analysis and multivariate statistical techniques. Eur. Acc. Rev., 11(3), 509–535 (2002b)CrossRefGoogle Scholar
  26. Stevens, D.L.: Financial characteristics of merged firms: a multivariate analysis. J. Financ. Quant. Anal. 8(02), 149–158 (1973)CrossRefGoogle Scholar
  27. Stice, J.D.: Using financial and market information to identify pre-engagement factors associated with lawsuits against auditors. Acc. Rev. 66, 516–533 (1991)Google Scholar
  28. The Institute of Internal Auditors: Practice Advisory 2320-1. Analysis and Evaluation, January 5 2001 (2001)Google Scholar
  29. Wells, J.T.: Principles of Fraud Examination. Wiley, Hoboken (2005)Google Scholar
  30. West, J., Bhattacharya, M., Islam, R.: Intelligent financial fraud detection practices: an investigation. In: Proceedings of the 10th International Conference on Security and Privacy in Communication Networks (SecureComm 2014) (2014)Google Scholar
  31. West, J., Bhattacharya, M., Islam, R.: Intelligent financial fraud detection practices: an investigation. In: International Conference on Security and Privacy in Communication Systems, pp. 186–203. Springer, Cham (2015)Google Scholar
  32. Zhang, G., Eddy Patuwo, B., Hu, M.V.: Forecasting with artificial neural networks: the state of the art. Int. J. Forecast. 14, 35–62 (1998)CrossRefGoogle Scholar
  33. Zhou, W., Kapoor, G.: Detecting evolutionary financial statement fraud. Decis. Support Syst. 50, 570–575 (2011)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Christianna Chimonaki
    • 1
  • Stelios Papadakis
    • 2
  • Konstantinos Vergos
    • 1
  • Azar Shahgholian
    • 3
    Email author
  1. 1.University of PortsmouthPortsmouthUK
  2. 2.Technology Educational Institute of CreteHeraklionGreece
  3. 3.Liverpool John Moores UniversityLiverpoolUK

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