(44-4) 22 * << * >> * Russian * English * Content * All Issues

Data mining of corporate financial fraud based on neural network model
S.L. Li 1

Accounting Department, Business School, Changchun Guanghua University, Changchun, Jilin 130033, China

 PDF, 1431 kB

DOI: 10.18287/2412-6179-CO-656

Pages: 665-670.

Full text of article: English language.

Under the active market economy, more and more listed companies emerge. Because of the various interest relationships faced by listed companies, some enterprises which are not well managed or want to enhance company’s value will choose to forge financial reports by improper means. In order to find out the false financial reports as accurately as possible, this paper briefly introduced the relevant indicators for judging the fraudulence of financial reports of listed companies and the recognition model of financial reports based on back propagation (BP) neural network. Then the selection of the input relevant indexes was improved. The improved BP neural network was simulated and analyzed in MATLAB software and compared with the traditional BP neural network and support vector machine (SVM). The results showed that the importance of total assets net profit, earnings per share, cash reinvestment rate, operating gross profit and pre-tax ratio of profit to debt was the top 5 among 20 judgment indexes. In the identification of testing samples of financial report, the accuracy, precision, recall rate and F value all showed that the performance of the improved BP neural network was better than that of the traditional BP network and SVM.

back propagation neural network, financial indicators, financial report fraud, data mining.

Li SL. Data mining of corporate financial fraud based on neural network model. Computer Optics 2020; 44(4): 665-670. DOI: 10.18287/2412-6179-CO-656.


  1. Fanning K, Cogger KO. Neural network detection of management fraud using published financial data. Intell Syst Account Finance Manag 2015; 7(1): 21-41.
  2. Qi J, Yi L. Network financial fraud risk assessment system based on big data analysis. J Comput Theor Nanosci 2016; 13(12): 9335-9339.
  3. Zanin M, Romance M, Moral S, et al. Credit card fraud detection through parenclitic network analysis. Complexity 2018; 2018: 1-9.
  4. Bahnsen AC, Aouada D, Ottersten B. Example-dependent cost-sensitive decision trees. Expert Syst Appl 2015; 42(19): 6609-6619.
  5. Fanning K, Cogger KO, Srivastava R. Detection of management fraud: A neural network approach. Intell Syst Account Finance Manag 1995; 4(2): 113-126.
  6. Kanapickienė R, Grundienė Ž. The model of fraud detection in financial statements by means of financial ratios. Procedia Soc Behav Sci 2015; 213: 321-327.
  7. Lin C, Chiu A, Huang SY, et al. Detecting the financial statement fraud: The analysis of the differences between data mining techniques and experts’ judgments. Knowl Based Syst 2015; 89(9): 459-470.
  8. Coakley JR, Brown CE. Artificial neural networks applied to ratio analysis in the analytical review process. Intell Syst Account Finance Manag 2015; 2(1): 19-39.
  9. Compin F. Tax fraud: A socially acceptable financial crime in France. J Financ Crime 2015; 22(4): 432-446.
  10. Chen T, Xu W. Post-evaluation on financial support highway traffic project based on BP neural network algorithm. J Discret Math Sci Cryptogr 2018; 21(4): 869-879.
  11. Hong Y, Sun W, Bai QL, Mu XW. SOM-BP neural network-based financial early-warning for listed companies. J Comput Theor Nanosci 2016; 13(10): 6860-6866.
  12. Wang L, Liu H, Feng C, et al. Identification of flow regimes based on adaptive learning and additional momentum BP neural network. 6th IMCCC 2016: 574-578.
  13. Liu SJ, Li SL, Jiang M, et al. Quantitative identification of pipeline crack based on BP neural network. Key Eng Mater 2017; 737: 477-480.
  14. He G, Huang C, Guo L, et al. Identification and adjustment of guide rail geometric errors based on BP neural network. Meas Sci Rev 2017; 17(3): 135-144.
  15. Yacoub HA, Sadek MA. Identification of fraud (with pig stuffs) in chicken-processed meat through information of mitochondrial cytochrome b. Mitochondrial DNA A DNA Mapp Seq Anal 2016; 28(6): 1.

© 2009, IPSI RAS
151, Molodogvardeiskaya str., Samara, 443001, Russia; E-mail: ko@smr.ru ; Tel: +7 (846) 242-41-24 (Executive secretary), +7 (846) 332-56-22 (Issuing editor), Fax: +7 (846) 332-56-20