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Fraud Detection Using Sequential Patterns from Credit Card Operations

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Intelligent Computing (SAI 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1228))

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Abstract

This paper presents a novel method for detection of frauds that uses the differences in temporal dependence (sequential patterns) between valid and non-legitimate credit card operations to increase the detection performance. A two-level fusion is proposed from the results of single classifiers. The first fusion is made in low-dimension feature spaces from the card operation record and the second fusion is made to combine the results obtained in each of the low-dimension spaces. It is assumed that sequential patterns are better highlighted in low-dimension feature spaces than in the high-dimension space of all the features of the card operation record. The single classifiers implemented were linear and quadratic discriminant analyses, classification tree, and naive Bayes. Alpha integration was applied to make an optimal combination of the single classifiers. The proposed method was evaluated using a real dataset with a great disproportion between non-legitimate and valid operations. The results were evaluated using the area under the receiver operating characteristic (ROC) curve of each of the single and fused results. We demonstrated that the proposed two-level fusion combining several low-dimension feature analyses outperforms the conventional analysis using the full set of features.

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References

  1. Ahmeda, M., Mahmooda, A.N., Islam, R.: A survey of anomaly detection techniques in financial domain. Future Gener. Comput. Syst. 55, 278–288 (2016)

    Article  Google Scholar 

  2. Bhattacharyya, S., Jha, S., Tharakunnel, K., Westland, J.C.: Data mining for credit card fraud: a comparative study. Decis. Supp. Syst. 50, 602–613 (2011)

    Article  Google Scholar 

  3. Bolton, R.J., Han, D.J.: Statistical fraud detection: a review. Stat. Sci. 17(3), 235–255 (2002)

    Article  MathSciNet  Google Scholar 

  4. Panigrahi, S., Kundu, A., Sural, S., Majumdar, A.K.: Credit card fraud detection: a fusion approach using Dempster-Shafer theory and Bayesian learning. Inf. Fusion 10, 354–363 (2009)

    Article  Google Scholar 

  5. Phua, C., Lee, V., Smith, K., Gayler, R.: A comprehensive survey of data mining-based fraud detection research. Comput. Res. Repos. 1–14 (2010)

    Google Scholar 

  6. Raj, S.B.E., Portia, A.A.: Analysis on credit card fraud detection methods. In: IEEE International Conference on Computer, Communication and Electrical Technology – ICCCET 2011, Tamil Nadu (India), pp. 152–156 (2011)

    Google Scholar 

  7. Wongchinsri, P., Kuratach, W.: A survey - data mining frameworks in credit card processing. In: 13th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON 2016, Chiang Mai, Thailand, pp. 1–6 (2016)

    Google Scholar 

  8. Salazar, A., Safont, G., Soriano, A., Vergara, L.: Automatic credit card fraud detection based on non-linear signal processing. In: International Carnahan Conference on Security Technology (ICCST), Boston, MA (USA), pp. 207–212 (2012)

    Google Scholar 

  9. Salazar, A., Safont, G., Vergara, L.: Surrogate techniques for testing fraud detection algorithms in credit card operations. In: International Carnahan Conference on Security Technology (ICCST), Rome (Italy), pp. 1–6 (2014)

    Google Scholar 

  10. Vergara, L., Salazar, A., Belda, J., Safont, G., Moral, S., Iglesias, S.: Signal processing on graphs for improving automatic credit card fraud detection. In: International Carnahan Conference on Security Technology (ICCST), Madrid (Spain), pp. 1–6 (2017)

    Google Scholar 

  11. Salazar, A., Safont, G., Rodriguez, A., Vergara, L.: Combination of multiple detectors for credit card fraud detection. In: International Symposium on Signal Processing and Information Technology (ISSPIT), Limassol (Cyprus), pp. 138–143 (2016)

    Google Scholar 

  12. Salazar, A., Safont, G., Vergara, L.: Semi-supervised learning for imbalanced classification of credit card transaction. In: International Joint Conference on Neural Networks (IJCNN), Rio de Janeiro (Brazil), pp. 1–7 (2018)

    Google Scholar 

  13. Salazar, A., Safont, G., Rodriguez, A., Vergara, L.: New perspectives of pattern recognition for automatic credit card fraud detection. In: Encyclopedia of Information Science and Technology, 4th edn., pp. 4937–4950, IGI Global (2018)

    Google Scholar 

  14. Dal Pozzolo, A., Caelen, O., Le Borgne, Y.-A., Waterschoot, S., Bontempi, G.: Learned lessons in credit card fraud detection from a practitioner perspective. Expert Syst. Appl. 41, 4915–4928 (2014)

    Article  Google Scholar 

  15. Safont, G., Salazar, A., Rodriguez, A., Vergara, L.: On recovering missing ground penetrating radar traces by statistical interpolation methods. Remote Sens. 6(8), 7546–7565 (2014)

    Article  Google Scholar 

  16. Safont, G., Salazar, A., Vergara, L., Gomez, E., Villanueva, V.: Probabilistic distance for mixtures of independent component analyzers. IEEE Trans. Neural Netw. Learn. Syst. 29(4), 1161–1173 (2018)

    Article  Google Scholar 

  17. Safont, G., Salazar, A., Vergara, L., Gómez, E., Villanueva, V.: Multichannel dynamic modeling of non-Gaussian mixtures. Pattern Recogn. 93, 312–323 (2019)

    Article  Google Scholar 

  18. Llinares, R., Igual, J., Salazar, A., Camacho, A.: Semi-blind source extraction of atrial activity by combining statistical and spectral features. Digit. Sig. Process.: Rev. J. 21(2), 391–403 (2011)

    Article  Google Scholar 

  19. Abry, P., Didier, G.: Wavelet estimation for operator fractional Brownian motion. Bernoulli 24(2), 895–928 (2018)

    Article  MathSciNet  Google Scholar 

  20. Wendt, H., Abry, P., Jaffard, S.: Bootstrap for empirical multifractal analysis. IEEE Sig. Process. Mag. 24(4), 38–48 (2007)

    Article  Google Scholar 

  21. Dubuisson, S.: Tracking with Particle Filter for High-Dimensional Observation and State Spaces. Digital Signal and Image Processing Series. Wiley, Hoboken (2015)

    Google Scholar 

  22. Amari, S.: Integration of stochastic models by minimizing α-divergence. Neural Comput. 19, 2780–2796 (2007)

    Article  MathSciNet  Google Scholar 

  23. Choi, H., Choi, S., Choe, Y.: Parameter learning for alpha integration. Neural Comput. 25(6), 1585–1604 (2013)

    Article  MathSciNet  Google Scholar 

  24. Soriano, A., Vergara, L., Bouziane, A., Salazar, A.: Fusion of scores in a detection context based on alpha-integration. Neural Comput. 27, 1983–2010 (2015)

    Article  Google Scholar 

  25. Safont, G., Salazar, A., Vergara, L.: Multiclass alpha integration of scores from multiple classifiers. Neural Comput. 31(4), 806–825 (2019)

    Article  MathSciNet  Google Scholar 

  26. Amari, S.: Information Geometry and Its Applications. Springer (2016)

    Google Scholar 

  27. Igual, J., Salazar, A., Safont, G., Vergara, L.: Semi-supervised Bayesian classification of materials with impact-echo signals. Sensors 15(5), 11528–11550 (2015)

    Article  Google Scholar 

  28. Salazar, A., Igual, J., Vergara, L., Serrano, A.: Learning hierarchies from ICA mixtures. In: IEEE International Joint Conference on Artificial Neural Networks, Orlando, FL (USA), pp. 2271–2276 (2007)

    Google Scholar 

  29. Salazar, A., Igual, J., Safont, G., Vergara, L., Vidal, A.: Image applications of agglomerative clustering using mixtures of non-Gaussian distributions. In: International Conference on Computational Science and Computational Intelligence (CSCI), Las Vegas, NV (USA), pp. 459–463 (2015)

    Google Scholar 

  30. Lesot, M.-J., d’Allonnes, A.R.: Credit-card fraud profiling using a hybrid incremental clustering methodology. In: International Conference on Scalable Uncertainty Management, Marburg (Germany), pp. 325–336 (2012)

    Google Scholar 

  31. Lui, H., Motoda, H.: Computational Methods of Feature Selection. CRC Press, Boca Rató (2007)

    Google Scholar 

  32. Maimon, O., Rokach, L.: Data Mining and Knowledge Discovery Handbook. Springer (2005)

    Google Scholar 

  33. Salazar, A., Gosalbez, J., Bosch, I., Miralles, R., Vergara, L.: A case study of knowledge discovery on academic achievement, student desertion and student retention. In: IEEE ITRE 2004 - 2nd International Conference on Information Technology: Research and Education, London (United Kingdom), pp. 150–154 (2004)

    Google Scholar 

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Acknowledgment

The Generalitat Valenciana supported this work under grant PROMETEO/2019/109.

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Authors and Affiliations

  1. Institute of Telecommunications and Multimedia Applications, Universitat Politècnica de València, Valencia, Spain

    Addisson Salazar, Gonzalo Safont & Luis Vergara

Authors
  1. Addisson Salazar
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  2. Gonzalo Safont
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  3. Luis Vergara
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Corresponding author

Correspondence to Addisson Salazar .

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Editors and Affiliations

  1. Saga University, Saga, Japan

    Kohei Arai

  2. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Supriya Kapoor

  3. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Rahul Bhatia

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Salazar, A., Safont, G., Vergara, L. (2020). Fraud Detection Using Sequential Patterns from Credit Card Operations. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Computing. SAI 2020. Advances in Intelligent Systems and Computing, vol 1228. Springer, Cham. https://doi.org/10.1007/978-3-030-52249-0_20

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