Financial Fraud Detection using Radial Basis Network

I. O. Alabi and R. G. Jimoh

Published in Volume 3 - Number 1, January 2018

DOI: https://doi.org/10.22632/ccs-2017-252-71

Subject Area : Artificial Intelligence, Security

PDF      BibTex      Citation

I. O. Alabi, R. G. Jimoh (2017). Financial Fraud Detection using Radial Basis Network. Circulation in Computer Science, 3, 1 (January 2018), 10-21. https://doi.org/10.22632/ccs-2017-252-71

     Cover      Share
     Promote

Abstract

The ubiquitous cases of abnormal transactions with intent to defraud is a global phenomenon. An architecture that enhances fraud detection using a radial basis function network was designed using a supervised data mining technique― radial basis function (RBF) network, a multivariate interpolation approximation method. Several base models were thus created, and in turn used in aggregation to select the optimum model using the misclassification error rate (MER), accuracy, sensitivity, specificity and receiver operating characteristics (ROC) metrics. The results shows that the model has a zero-tolerance for fraud with better prediction especially in cases where there were no fraud incidents doubtful cases were rather flagged than to allow a fraud incident to pass undetected. Expectedly, the model’s computations converge faster at 200 iterations. This study is generic with similar characteristics with other classification methods but distinct parameters thereby minimizing the time and cost of fraud detection by adopting computationally efficient algorithm.

Keywords: Artificial neural network, data mining, detecting fraud transactions, fraud detection , radial basis function network

References

  1. Anderson, J.A. & Rosenfeld, E., 1998. Neurocomputing: Foundations of Research. MIT Press, Cambridge.
  2. Apostolou, B., Hassell, J., Webber, S., & Sumners, G., (2001). The relative importance of management fraud risk factors. Behavioral Research in Accounting 13, 1–24.
  3. Ben-David, S., Loker D., Srebro, N., & Sridharam, K., (2012). Proceedings of the 29th International Conference on Machine Learning, Edinburgh, Scotland, U.K.
  4. Bolton, R., & Hand, D., (2002). Statistical Fraud Detection: A Review (With Discussion). Statistical Science 17(3): 235–255.
  5. Bishop, C.M., (1995). Neural Networks for Pattern Recognition. Oxford University Press, Oxford, UK.
  6. Buhmann, M. D., (2004). Radial basis functions: Theory and Implementations. Cambridge university press. ISBN 0-521-63338-9.
  7. Burge, P. & Shawe-Taylor, J., (2001). An Unsupervised Neural, Network Approach to Profiling the Behaviour of Mobile Phone, Users for Use in Fraud Detection. Journal of Parallel and Distributed Computing 61: 915–925.
  8. Chang, W. & Chang, J., (2012), An effective early fraud detection method for online auctions. Electronic Commerce Research and Applications 11 (2012) 346–360.
  9. Coderre, D., (2009), Computer-Aided fraud Prevention and Detection, John Wiley and Sons, Inc. Hoboken, New Jersey.
  10. Cox, K., Eick, S. & Wills, G., (1997). Visual Data Mining: Recognising Telephone Calling Fraud. Data Mining and Knowledge Discovery, 1: 225–231.
  11. Dorronsoro, J. R., Ginel, F., Sánchez C., & Cruz C. S., (1997). IEEE transactions on neural networks, 4(8) 827-834.
  12. Field, S., & Hobson, P., 1997. Techniques for telecommunications fraud management. In Proceedings of International Workshop on Applications of Neural Networks to Telecommunications 3, 107–115.
  13. Fawcett, T., & Provost, F., (1997). Combining data mining and machine learning for effective fraud detection. In AAAI Workshop on AI Approaches to Fraud Detection and Risk Management, 14–19).
  14. Gupta, R., & Gill, N. S., (2013), Prevention and Detention of Financial Statement Fraud – An implementation of Data Mining Framework. International Journal of Advanced Computer Science and Applications, 3(8), 65-76.
  15. Hand D., Mannila H. & Smyth P. (2001). Principles of Data Mining, A Bradford Book, Massachusetts Institute of Technology Press, London, England.
  16. Hackenbrack K.,( 1993). The effect of experience with different sized clients on auditor evaluations of fraudulent financial reporting indicators. Auditing: A Journal of Practice and Theory;1:99–110.
  17. Hippert, H.S., Bunn, D.W., & Souza, R.C., (2005). Large neural networks for electricity load forecasting: Are they overfitted. International Journal of Forecasting, 21, 425–434.
  18. Hoffman, H (2000). UCI Machine Learning Repository [http://archive. .ics.uci.edu/ml]. Institut f"ur Statistik und "Okonometrie Universit"at Hamburg.
  19. Khac, N. L. & Kechadi, M., (2010). Application of data mining for anti-money laundering detection: A case study. 2010 IEEE international conference on data mining workshops. 577-584.
  20. Krambia-Kapardis, M., Christodoulou, C., & Agathocleous, M. (2010). Neural networks: the panacea in fraud detection. Managerial Auditing Journal 2010;7: 659–78.
  21. Loebbecke, J.K., Einning, M.M.& Willingham, J. J., (1989). Auditors’ experience with material irregularities: frequency, nature and detectability. Auditing: A Journal of Practice & Theory;1:1–28.
  22. Majid, A, Gul, F.A. & Tsui, J., ( 2001). An analysis of Hong Kong auditors’ perceptions of the importance of selected red flag factors in risk assessment. Journal of Business Ethics;3:263–74.
  23. Maranzato, R., Pereira, A., Naubert, M., & Lago, A. P., (2010). Fraud detection in reputation systems in e-markets using logistic regression and stepwise optimization. In ACM SIGAPP Applied Computing Review.
  24. Mock, T.J. & Turner J.L. (2005). Auditor identification of fraud risk factors and their impact on audit programs. International Journal of Auditing 2005;9:59–77.
  25. Moyes, G.L. (2007). The differences in perceived level of fraud-detecting effectiveness of SAS No. 99 red flags between external and internal auditors. Journal of Business & Economics Research; 6:9–25.
  26. Murad, U. & Pinkas, G. (1999). Unsupervised Profiling for Identifying Superimposed Fraud. Proceedings of PKDD'99.
  27. Nigrini, M., (2011). Forensic Analytics: Methods and Techniques for Forensic Accounting Investigations. Hoboken, NJ: John Wiley & Sons Inc. ISBN 978-0-470-89046-2.
  28. Oussar y., & Dreyfus G. (2002). Initialization by selection for wavelet network training, Neurocomputing, 34, 131–143.
  29. Pincus KV., (1989). The efficacy of a red flags questionnaire for assessing the possibility of fraud. Accounting, Organizations and Society 1989; 14:153–64.
  30. Phua, C., Lee V., Smith, K., & Gayler, R., (2005). A comprehensive survey of data mining-based fraud detection research. Artificial Intelligence Review (2005) 1–14.
  31. Shen, A., Tong, R., & Deng, Y., (2007). Application of classification models on credit card fraud detection. In Proceedings of the 10th International Conference on Service Systems and Service Management,1–4.
  32. Sherly, K.K., Nedunchezhian, R., (2010). Boat adaptive credit card fraud detection system. IEEE (2010).
  33. Smith, M., Omar, N.H., Idris, S., & Baharuddin, I., (2005). Auditors’ perception of fraud risk indicators, Malaysian evidence. Managerial Auditing Journal 2005;1:73–85.
  34. SPSS, Statistical Package for the Social Sciences (2000). Using data mining to detect fraud. White paper -technical report. SPSS Inc., USA.
  35. Stern, H.S., (1996). Neural networks in applied statistics. Technometrics 38 (3), 205–216.
  36. Sumathi S. & Sivanandam S. N., (2006). Introduction to data mining and its applications, Studies in Computational Intelligence, Volume 29, ISBN 3-540-34350-4.
  37. Taniguchi, M., Haft, M., HollmTn, J., & Tresp, V., (1998). Fraud detection in communications networks using neural and probabilistic methods. In Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing 2, (pp. 1241–1244).
  38. Viaene, S., Dedene, G. Dedene, & Derring R. A., (2005). Auto claim Fraud detection using Bayesian learning neural networks. Expert systems with applications 29 (2005) 653-666.
  39. Wilson, J. H., (2009). An analytical approach to detecting insurance fraud using logistic regression. Journal of Finance and Accountancy, 1.
  40. Yue, X. Wu, Y. Wang, Y. Li, & Chu C., (2007). A review of data mining-based financial fraud detection research. International conference on wireless communications Sep, Networking and Mobile Computing. 5519–5522.

Submit paper

Paper submission by August 25, 2018

Submit Now