Applying Migrating Birds Optimization to Credit Card Fraud Detection

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7867)


We discuss how the Migrating Birds Optimization algorithm (MBO) is applied to statistical credit card fraud detection problem. MBO is a recently proposed metaheuristic algorithm which is inspired by the V flight formation of the migrating birds and it was shown to perform very well in solving a combinatorial optimization problem, namely the quadratic assignment problem. As analyzed in this study, it has a very good performance in the fraud detection problem also when compared to classical data mining and genetic algorithms. Its performance is further increased by the help of some modified neighborhood definitions and benefit mechanisms.


migrating birds optimization algorithm fraud credit cards genetic algorithms 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Duman, E., Uysal, M., Alkaya, A.: Migrating Birds Optimization: A new metaheuristic approach and its performance on quadratic assignment problem. Information Sciences 217, 65–77 (2012)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Bolton, R.J., Hand, D.J.: Statistical fraud detection: A review. Statistical Science 28(3), 235–255 (2002)MathSciNetGoogle Scholar
  3. 3.
    Kou, Y., et al.: Survey of fraud detection techniques. In: Proceedings of the 2004 IEEE International Conference on Networking, Sensing and Control, Taipei, Taiwan, March 21-23 (2004)Google Scholar
  4. 4.
    Phua, C., et al.: A comprehensive survey of data mining-based fraud detection research. Artificial Intelligence Review (2005)Google Scholar
  5. 5.
    Sahin, Y., Duman, E.: An overview of business domains where fraud can take place, and a survey of various fraud detection techniquesGoogle Scholar
  6. 6.
    Brause, R., Langsdorf, T., Hepp, M.: Neural data mining for credit card fraud detection. In: Proceedings of the 11th IEEE International Conference on Tools with Artificial Intelligence (1999)Google Scholar
  7. 7.
    Hanagandi, V., Dhar, A., Buescher, K.: Density-Based Clustering and Radial Basis Function Modeling to Generate Credit Card Fraud Scores. In: Proceedings of the IEEE/IAFE 1996 Conference (1996)Google Scholar
  8. 8.
    Juszczak, P., Adams, N.M., Hand, D.J., Whitrow, C., Weston, D.J.: Off-the-peg and bespoke classifiers for fraud detection. Computational Statistics & Data Analysis 52(9) (2008)Google Scholar
  9. 9.
    Quah, J.T., Sriganesh, M.: Real-time credit card fraud detection using computational intelligence. Expert Systems with Applications 35(4) (2008)Google Scholar
  10. 10.
    Shen, A., Tong, R., Deng, Y.: Application of classification models on credit card fraud detection. In: International Conference on Service Systems and Service Management, Chengdu, China (June 2007)Google Scholar
  11. 11.
    Wheeler, R., Aitken, S.: Multiple algorithms for fraud detection. Knowledge-Based Systems 13(2/3) (2000)Google Scholar
  12. 12.
    Chen, R.-C., Chiu, M.-L., Huang, Y.-L., Chen, L.-T.: Detecting credit card fraud by using questionnaire-responded transaction model based on SVMs. In: Yang, Z.R., Yin, H., Everson, R.M. (eds.) IDEAL 2004. LNCS, vol. 3177, pp. 800–806. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  13. 13.
    Han, J., Camber, M.: Data mining concepts and techniques. Morgan Kaufman, San Diego (2000)Google Scholar
  14. 14.
    Quah, J.T.S., Srinagesh, M.: Real-time credit fraud detection using computational intelligence. Expert Systems with Applications 35, 1721–1732 (2008)CrossRefGoogle Scholar
  15. 15.
    Panigrahi, S., Kundu, A., Sural, S., Majumdar, A.: Credit Card Fraud Detection: A Fusion Approach Using Dempster-Shafer Theory and Bayesian Learning. Information Fusion, 354–363 (2009)Google Scholar
  16. 16.
    Sanchez, D., Vila, M.A., Cerda, L., Serrano, J.M.: Association rules applied to credit card fraud detection. Expert Systems with Applications 36, 3630–3640 (2009)CrossRefGoogle Scholar
  17. 17.
    Kim, M., Han, I.: The Discovery of Experts’ Decision Rules from Qualitative Bankrupcy Data Using Genetic Algorithms. Expert Systems with Applications 25, 637–646 (2003)CrossRefGoogle Scholar
  18. 18.
    Gadi, M.F.A., Wang, X., do Lago, A.P.: Credit Card Fraud Detection with Artificial Immune System. In: Bentley, P.J., Lee, D., Jung, S. (eds.) ICARIS 2008. LNCS, vol. 5132, pp. 119–131. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  19. 19.
    Duman, E., Ozcelik, M.H.: Detecting credit card fraud by genetic algorithm and scatter search. Expert Systems with Applications 38, 13057–13063 (2011)CrossRefGoogle Scholar
  20. 20.
    Duman, E., Sahin, Y.: A Comparison of Classification Models on Credit Card Fraud Detection with respect to Cost-Based Performance Metrics. In: Duman, E., Atiya, A. (eds.) Use of Risk Analysis in Computer-Aided Persuasion. NATO Science for Peace and Security Series E: Human and Societal Dynamics, vol. 88, pp. 88–99. IOS Press (2011)Google Scholar
  21. 21.
    Gartner Reports, May 10, 2010 from the World Wide Web,
  22. 22.
    Mena, J.: Investigate Data Mining for Security and Criminal Detection. Butterworth-Heinemann, Amsterdam (2003)Google Scholar
  23. 23.
    Ling, C.X., Sheng, V.S., Yang, Q.: Test Strategies for Cost-Sensitive Decision Trees. IEEE Transactions on Knowledge and Data Engineering 18(8) (2006)Google Scholar
  24. 24.
    Liu, X.: A Benefit-Cost Based Method for Cost-Sensitive Decision Trees. In: 2009 WRI Global Congress on Intelligent Systems, pp. 463–467 (2009)Google Scholar
  25. 25.
    Cutts, C.J., Speakman, J.R.: Energy savings in formation flight of pink-footed geese. J. Exp. Biol. 189, 251–261 (1994)Google Scholar
  26. 26.
    Lissaman, P.B.S., Shollenberger, C.A.: Formation flight of birds. Science 168, 1003–1005 (1970)CrossRefGoogle Scholar
  27. 27.
    Hummel, D., Beukenberg, M.: Aerodynamsiche Interferenseffekte beim formationsflug von vögeln. J. Orn. 130, 15–24 (1989)CrossRefGoogle Scholar
  28. 28.
    Karaboga, D., Basturk, B.: A powerful and efficient algorithm for numerical function optimization: Artificial Bee Colony (ABC) Algorithm. Journal of Global Optimization 39(3), 459–471 (2007)MathSciNetzbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Faculty of Engineering, Industrial Engineering DepartmentÖzyeğin UniversityIstanbulTurkey
  2. 2.Intertech, Decision Support Systems DepartmentIstanbulTurkey

Personalised recommendations