Integrating Knowledge Engineering and Data Mining in e-commerce Fraud Prediction

  • Timo Polman
  • Marco Spruit
Part of the Communications in Computer and Information Science book series (CCIS, volume 278)


The number of merchants and consumers that participate in b2c e-commerce is still growing. Overall fraud rates have stabilized in recent years but for post-payment transactions in the Netherlands the fraud percentage remains unacceptably high. Companies often have a great deal of knowledge about fraudulent orders, and how to recognize them. Fraud prevention is often aided by automated recognition systems that are created through data mining. There have been few studies examining the combination of explicit domain knowledge and data mining. This study analyses the incorporation of domain knowledge in data mining for fraud prediction based on a historical dataset of 5,661 post-payment orders.


Data mining knowledge discovery in databases knowledge engineering automated fraud detection 


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  1. 1.
    Pazzani, M., Kibler, D.: The Utility of Knowledge in Inductive Learning. Machine Learning 9, 57–94 (1992)Google Scholar
  2. 2.
    Alonso, F., Caraça-Valente, J.P., González, A.L., Montes, C.: Combining expert knowledge and data mining in a medical diagnosis domain. Expert Systems with Applications 23, 367–375 (2002)CrossRefGoogle Scholar
  3. 3.
    Chapman, P., Clinton, J., Kerber, R., Khabaza, T., Reinartz, T., Shearer, C., Wirth, R.: CRISP-DM 1.0 Step-by-step data mining guide (1999),
  4. 4.
    Daniëls, H.A.M., Feelders, A.J.: Integrating Economic Knowledge in Data Mining Algorithms. Tilburg University, Center for Economic Research (2001)Google Scholar
  5. 5.
    Dinu, V., Zhao, H., Miller, P.L.: Integrating domain knowledge with statistical and data mining methods for high-density genomic SNP disease association analysis. Journal of Biomedical Informatics 40, 750–760 (2007)CrossRefGoogle Scholar
  6. 6.
    Fayyad, U., Piatetsky-Shapiro, G., Smyth, P.: The KDD process for extracting useful knowledge from volumes of data. Commun. ACM. 39, 27–34 (1996)CrossRefGoogle Scholar
  7. 7.
    Kopanas, I., Avouris, N., Daskalaki, S.: The Role of Domain Knowledge in a Large Scale Data Mining Project. In: Vlahavas, I.P., Spyropoulos, C.D. (eds.) SETN 2002. LNCS (LNAI), vol. 2308, pp. 288–299. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  8. 8.
    Langseth, H., Nielsen, T.D.: Fusion of Domain Knowledge with Data for Structural Learning in Object Oriented Domains. Journal of Machine Learning Research 4, 339–368 (2003)MathSciNetGoogle Scholar
  9. 9.
    Sinha, A.P., Zhao, H.: Incorporating domain knowledge into data mining classifiers: An application in indirect lending. Decision Support Systems 46, 287–299 (2008)CrossRefGoogle Scholar
  10. 10.
    Chan, P.K., Wei Fan, A.L., Stolfo, J.: Distributed Data Mining in Credit Card Fraud Detection. IEEE Intelligent Systems and Their Applications 1094, 67–74 (1999)CrossRefGoogle Scholar
  11. 11.
    Quah, J.T.S., Sriganesh, M.: Real-time credit card fraud detection using computational intelligence. Expert Systems with Applications 35, 1721–1732 (2008)CrossRefGoogle Scholar
  12. 12.
    Sánchez, 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
  13. 13.
    Schreiber, G., Akkermans, H., Anjewierden, A., de Hoog, R., Shadbolt, N., Van de Velde, W., Wielinga, B.: Knowledge engineering and management. MIT Press, London (2000)Google Scholar
  14. 14.
    Schweickert, R., Burton, A.M., Taylor, N.K., Corlett, E.N., Shadbolt, N.R., Hedgecock, A.P.: Comparing knowledge elicitation techniques: a case study. Artif. Intell. Rev. 1, 245–253 (1987)CrossRefGoogle Scholar
  15. 15.
    Pang-Ning, T., Steinbach, M., Kumar, V.: Classification: Alternative Techniques. Data Mining, ch. 5, pp. 207–326. Addison Wesley (2005)Google Scholar
  16. 16.
    Moore, A.: Decision Trees Tutorial Slides (2005),
  17. 17.
    Fayyad, U., Stolorz, P.: Data mining and KDD: Promise and challenges. Future Generation Computer Systems 13, 99–115 (1997)CrossRefGoogle Scholar
  18. 18.
    Kirkos, E., Spathis, C., Manolopoulos, Y.: Data Mining techniques for the detection of fraudulent financial statements. Expert Systems with Applications 32, 995–1003 (2007)CrossRefGoogle Scholar
  19. 19.
    Provost, F., Fawcett, T.: Analysis and visualization of classifier performance: Comparison under imprecise class and cost distributions. Presented at the Proceedings of the Third International Conference on Knowledge Discovery and Data Mining (1997)Google Scholar
  20. 20.
    Provost, F., Domingos, P.: Tree Induction for Probability-Based Ranking. Machine Learning 52, 199–215 (2003)zbMATHCrossRefGoogle Scholar
  21. 21.
    Fawcett, T.: An introduction to ROC analysis. Pattern Recognition Letters 27, 861–874 (2006)CrossRefGoogle Scholar
  22. 22.
    Ambrosino, R., Buchanan, B.G.: The use of physician domain knowledge to improve the learning of rule-based models for decision-support. In: Proc. AMIA Symp., pp. 192–196 (1999)Google Scholar
  23. 23.
    Phua, C., Lee, V., Smith, K., Gayler, R.: A comprehensive survey of data mining-based fraud detection research. Artificial Intelligence Review (2005)Google Scholar
  24. 24.
    Weiss, G., Provost, F.: The effect of class distribution on classifier learning: an empirical study. Rutgers Univ. (2001)Google Scholar
  25. 25.
    Nadeau, C., Bengio, Y.: Inference for the Generalization Error. Machine Learning 52, 239–281 (2003)zbMATHCrossRefGoogle Scholar
  26. 26.
    Demšar, J.: Statistical comparisons of classifiers over multiple data sets. The Journal of Machine Learning Research 7, 30 (2006)Google Scholar
  27. 27.
    Stolte, V.: Onderzoek naar een e-commerce fraudedetectie strategie (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Timo Polman
    • 1
  • Marco Spruit
    • 1
  1. 1.Institute of Information and Computing SciencesUtrecht UniversityUtrechtThe Netherlands

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