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Fraud Detection in Networks

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Enabling AI Applications in Data Science

Part of the book series: Studies in Computational Intelligence ((SCI,volume 911))

Abstract

Financial fraud detection represents the challenge of finding anomalies in networks of financial transactions. In general, anomaly detection is the problem of distinguishing between normal data samples with well defined patterns or signatures and those that do not conform to the expected profiles. The fraudulent behavior in money laundering may manifest itself through unusual patterns in financial transaction networks. Most commonly, these networks are represented as attributed graphs, with numerical features complementing relational information. We present a survey on anomaly detection techniques used for fraud detection that exploit both the graph structure underlying the data and the contextual information contained in the attributes. While the focus in on methods that are suited for financial frauds, we extend the review to other types of frauds on networks.

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References

  1. Akoglu, L., McGlohon, M., Faloutsos, C.: Oddball: Spotting anomalies in weighted graphs. In: Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 410–421 (2010)

    Google Scholar 

  2. Akoglu, L., Tong, H., Koutra, D.: Graph based anomaly detection and description: a survey. Data Min. Knowl. Discov. 29, 626–688 (2014)

    Article  MathSciNet  Google Scholar 

  3. Akoglu, L., Tong, H., Meeder, B., Faloutsos, C.: PICS: Parameter-free Identification of Cohesive Subgroups in Large Attributed Graphs. In: Proceedings of the 2012 SIAM International Conference on Data Mining, pp 439–450 (2012)

    Google Scholar 

  4. Amer, M., Goldstein, M., Abdennadher, S.: Enhancing one-class support vector machines for unsupervised anomaly detection. In: Proceedings of the ACM SIGKDD Workshop on Outlier Detection and Description, pp. 8–15 (2013)

    Google Scholar 

  5. Ban, Y., Liu, X., Duan, Y., Liu, X., Xu, W.: No place to hide: catching fraudulent entities in tensors. In: The World Wide Web Conference, pp. 83–93 (2019)

    Google Scholar 

  6. Bhatia, S., Hooi, B., Yoon, M., Shin, K., Faloutsos, C.: Midas: microcluster-based detector of anomalies in edge streams. In: Association for the Advancement of Artificial Intelligence (2020)

    Google Scholar 

  7. Băltoiu, A., Pătraşcu, A., Irofti, P.: Graph anomaly detection using dictionary learning. In: The 21st World Congress of the International Federation of Automatic Control, pp. 1–8 (2020)

    Google Scholar 

  8. Cao, B., Mao, M., Viidu, S., Yu, P.S.: Collective fraud detection capturing inter-transaction dependency. In: Proceedings of Machine Learning Research, KDD 2017, vol. 71, pp. 66–75 (2017)

    Google Scholar 

  9. Chen, J., Saad, Y.: Dense subgraph extraction with application to community detection. IEEE Trans. Knowl. Data Eng. 24(7), 1216–1230 (2012)

    Article  Google Scholar 

  10. Chen, Z., Hendrix, W., Samatova, N.F.: Community-based anomaly detection in evolutionary networks. J. Intell. Inf. Syst. 39(1), 59–85 (2012)

    Article  Google Scholar 

  11. Colladon, A.F., Remondi, E.: Using social network analysis to prevent money laundering. Expert Syst. Appl. 67, 49–58 (2017)

    Article  Google Scholar 

  12. Cucuringu, M., Blondel, V.D., Van Dooren, P.: Extracting spatial information from networks with low order eigenvectors. Phys. Rev. E 87, 032803 (2013)

    Google Scholar 

  13. Delamaire, L., Abdou, H., Pointon, J.: Credit card fraud and detection techniques: a review. Banks Bank Syst. 4, 57–68 (2009)

    Google Scholar 

  14. Dhawan, S., Gangireddy, S.C.R, Kumar, S., Chakraborty, T.: Spotting collective behaviour of online frauds in customer reviews. In: Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI-19), pp. 245–251 (2019)

    Google Scholar 

  15. Ding, K., Li, J., Liu, H.: Interactive anomaly detection on attributed networks. In: Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, pp. 357–365 (2019)

    Google Scholar 

  16. Dumitrescu, B., Irofti, P.: Dictionary Learning Algorithms and Applications. Springer (2018)

    Google Scholar 

  17. Elliott, A., Cucuringu, M.C., Luaces, M.M., Reidy, P., Reinert, G.: Anomaly detection in networks with application to financial transaction networks. Arxiv: arXiv:1901.00402 [stat.AP] (2018)

  18. Erfani, S.M., Rajasegarar, S., Karunasekera, S., Leckie, C.: High-dimensional and large-scale anomaly detection using a linear one-class svm with deep learning. Pattern Recognit. 58(C), 121–134 (2016)

    Google Scholar 

  19. Eswaran, D., Faloutsos, C.: Sedanspot: detecting anomalies in edge streams. In: IEEE International Conference on Data Mining (ICDM), pp. 953–958 (2018)

    Google Scholar 

  20. European Central Bank. Ecb report shows a fall in card fraud in 2016. https://www.ecb.europa.eu/press/pr/date/2018/html/ecb.pr180926.en.html, 26 September 2018. Accessed 29 Feb 2020

  21. Flegel, U., Vayssiere, J., Bitz, G.: A state of the art survey of fraud detection technology. In Probst, C., Hunker, J., Gollmann, D., Bishop, M. (eds.) Insider Threats in Cyber Security, pp. 73–84. Springer (2010)

    Google Scholar 

  22. Gao, J., Du, N., Fan, W., Turaga, D., Parthasarathy, S., Han, J.: A multi-graph spectral framework for mining multi-source anomalies. In: Graph Embedding for Pattern Analysis, pp. 205–227. Springer (2013)

    Google Scholar 

  23. Guo, Q., Li, Z., An, B., Hui, P., Huang, J., Zhang, L., Zhao, M.: Securing the deep fraud detector in large scale e-commerce platform via adversarial machine learning approach. In: Proceedings of the 2019 World Wide Web Conference (WWW’19), pp. 616–626 (2019)

    Google Scholar 

  24. Hejazi, M., Singh, Y.P.: One-class support vector machines approach to anomaly detection. Appl. Artif. Intell. 27(5), 351–366 (2013)

    Article  Google Scholar 

  25. Henderson, K., Gallagher, B., Li, L., Akoglu, L., Eliassi-Rad, T., Tong, H., Faloutsos, C.: It’s who you know: graph mining using recursive structural features. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’11, pp. 663–671. New York, NY, USA. Association for Computing Machinery (2011)

    Google Scholar 

  26. Huang, Z., Ye, Y., Li, X., Liu, F., Chen, H.: Joint weighted nonnegative matrix factorization for mining attributed graphs. In: Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 368–380 (2017)

    Google Scholar 

  27. Ionescu, R.T., Smeureanu, S., Alexe, B., Popescu, M.: Unmasking the abnormal events in video. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2895–2903 (2017)

    Google Scholar 

  28. Irofti, P., Băltoiu, A.: Malware identification with dictionary learning. In: 27th European Signal Processing Conference, pp. 1–5 (2019)

    Google Scholar 

  29. Irofti, P., Băltoiu, A.: Unsupervised dictionary learning for anomaly detection. Arxiv: arXiv:2003.00293 (2019)

  30. Irofti, P., Stoican, F.: Dictionary learning strategies for sensor placement and leakage isolation in water networks. In: The 20th World Congress of the International Federation of Automatic Control, pp. 1589–1594 (2017)

    Google Scholar 

  31. Jiang, M., Cui, P., Beutel, A., Faloutsos, C., Yang, S.: Inferring strange behavior from connectivity pattern in social networks. In: Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 126–138. Springer (2014)

    Google Scholar 

  32. Jiang, M., Cui, P., Beutel, A., Faloutsos, C., Yang, S.: Catching synchronized behaviors in large networks: a graph mining approach. ACM Trans. Knowl. Discov. Data 10(4), 1–27 (2016)

    Article  Google Scholar 

  33. Jun, T., Jian, Y.: Developing an intelligent data discriminating system of anti-money laundering based on SVM. In: 2005 International Conference on Machine Learning and Cybernetics, vol. 6, pp. 3453–3457 (2005)

    Google Scholar 

  34. Kocsis, L., György, A.: Fraud detection by generating positive samples for classification from unlabeled data. In: Proceedings of the 27th International Conference on Machine Learning. Workshop on Machine Learning and Games (2010)

    Google Scholar 

  35. Lamrini, B., Gjini, A., Daudin, S., Pratmarty, P., Armando, F., Travé-Massuyès, L.: Anomaly detection using similarity-based one-class svm for network traffic characterization. In: 29th International Workshop on Principles of Diagnosis (2018)

    Google Scholar 

  36. Larik, A.S., Haider, S.: Clustering based anomalous transaction reporting. Procedia Comput. Sci. 3, 606–610 (2011)

    Article  Google Scholar 

  37. Latimer, P.: Australia: Australian transaction reports and analysis centre (austrac). J. Financ. Crime 3, 306–307 (1996)

    Article  Google Scholar 

  38. Li, J., Dani, H., Hu, X., Liu, H.: Radar: residual analysis for anomaly detection in attributed networks. In: Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, pp. 2152–2158 (2017)

    Google Scholar 

  39. Li, N., Sun, H., Chipman, K.C., George, J., Yan, X.: A probabilistic approach to uncovering attributed graph anomalies. In: Proceedings of the 2014 SIAM International Conference on Data Mining, pp. 82–90 (2014)

    Google Scholar 

  40. Li, Z., Xiong, H., Liu, Y., Zhou, A.: Detecting blackhole and volcano patterns in directed networks. In: 2010 IEEE International Conference on Data Mining, pp. 294–303 (2010)

    Google Scholar 

  41. Liu, N., Huang, X., Hu, X.: Accelerated local anomaly detection via resolving attributed networks. In: Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, pp. 2337–2343 (2017)

    Google Scholar 

  42. Liu, S., Hooi, B., Faloutsos, C.: Holoscope: Topology-and-spike aware fraud detection. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp. 1539–1548 (2017)

    Google Scholar 

  43. Miller, B.A., Arcolano, N., Bliss, N.T.: Efficient anomaly detection in dynamic, attributed graphs: emerging phenomena and big data. In: IEEE International Conference on Intelligence and Security Informatics, pp. 179–184 (2013)

    Google Scholar 

  44. Miller, B.A., Beard, M.S., Bliss, N.T.: Eigenspace analysis for threat detection in social networks. In: Proceedings of the 14th International Conference on Information Fusion (FUSION), pp. 1–7 (2011)

    Google Scholar 

  45. Miller, B.A., Beard, M.S., Wolfe, P.J., Bliss, N.T.: A spectral framework for anomalous subgraph detection. IEEE Trans. Signal Process. 63(16), 4191–4206 (2015)

    Article  MathSciNet  Google Scholar 

  46. Miller, B.A., Bliss, N.T., Wolfe, P.J.: Toward signal processing theory for graphs and non-euclidean data. In: IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), pp. 5414–5417 (2010)

    Google Scholar 

  47. Ngai, EWT., Hu, Y., Wong, Y.H., Chen, Y., Sun, X.: The application of data mining techniques in financial fraud detection: a classification framework and an academic review of literature. Decis. Support Syst. 50(02), 559–569 (2011)

    Google Scholar 

  48. Nilforoshan, H., Shah, N.: Slicendice: mining suspicious multi-attribute entity groups with multi-view graphs. In: 2019 IEEE International Conference on Data Science and Advanced Analytics (DSAA), pp. 351–363 (2019)

    Google Scholar 

  49. Pandit, S., Chau, D.H., Wang, S., Faloutsos, C. Netprobe: a fast and scalable system for fraud detection in online auction networks. In: Proceedings of the 16th international conference on World Wide Web, pp. 201–210 (2007)

    Google Scholar 

  50. Pastor-Satorras, R., Castellano, C.: Distinct types of eigenvector localization in networks. Sci. Rep. 6 (2016)

    Google Scholar 

  51. Peng, Z., Luo, M., Li, J., Liu, H., Zheng, Q.: Anomalous: a joint modeling approach for anomaly detection on attributed networks. In: Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, IJCAI-18, pp. 3513–3519 (2018)

    Google Scholar 

  52. Perozzi, B., Akoglu, L.: Scalable anomaly ranking of attributed neighborhoods. In: Proceedings of the 2016 SIAM International Conference on Data Mining, pp. 207–215 (2016)

    Google Scholar 

  53. Phua, C., Lee, V., Smith, K., Gayler, R.: A comprehensive survey of data mining-based fraud detection research. Intell. Comput. Technol. Autom. (ICICTA), pp. 50–53 (2010)

    Google Scholar 

  54. Pimentel, T., Monteiro, M., Viana, J., Veloso, A., Ziviani, N.: A generalized active learning approach for unsupervised anomaly detection. CoRR, abs/1805.09411 (2018)

    Google Scholar 

  55. Qiu, X., Cen, W., Qian, Z., Peng, Y., Zhang, Y., Lin, X., Zhou, J.: Real-time constrained cycle detection in large dynamic graphs. Proc. VLDB Endow. 11(12), 1876–1888 (2018)

    Article  Google Scholar 

  56. Savage, D., Wang, Q., Chou, P., Zhang, X., Yu, X.: Detection of money laundering groups using supervised learning in networks. arXiv preprint arXiv:1608.00708 (2016)

  57. Sengupta, S.: Anomaly Detection in Static Networks using Egonets (2018)

    Google Scholar 

  58. Shah, N., Beutel, A., Hooi, B., Akoglu, L., Günnemann, S., Makhija, D., Kumar, M., Faloutsos, C.: Edgecentric: anomaly detection in edge-attributed networks. In: 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW), pp. 327–334 (2016)

    Google Scholar 

  59. Shin, K., Eliassi-Rad, T., Faloutsos, C.: Patterns and anomalies in k-cores of real-world graphs with applications. Knowl. Inf. Syst. 677–710 (2017)

    Google Scholar 

  60. Skretting, K., Engan, K.: Intrusion detection in computer networks by a modular ensemble of one-class classifiers. Inf. Fus. 9(1), 69–82 (2008)

    Article  Google Scholar 

  61. Sorournejad, S., Zojaji, Z., Atani, R.E., Monadjemi, A.H.: A survey of credit card fraud detection techniques: data and technique oriented perspective. arXiv: abs/1611.06439 (2016)

  62. Tian, Y., Mirzabagheri, M., Bamakan, H., Wang, S.M.H., Qu, Q.: Ramp loss one-class support vector machine; a robust and effective approach to anomaly detection problems. Neurocomputing 310, 223–235 (2018)

    Google Scholar 

  63. Tong, H., Lin, C.-Y.: Non-negative residual matrix factorization with application to graph anomaly detection. In: Proceedings of the 2011 SIAM International Conference on Data Mining, pp. 143–153 (2011)

    Google Scholar 

  64. Velampalli, S., Eberle, W.: Novel graph based anomaly detection using background knowledge. In: FLAIRS Conference (2017)

    Google Scholar 

  65. Vengertsev, D., Thakkar, H.: Anomaly detection in graph: unsupervised learning, graph-based features and deep architecture. Tech. Rep. (2015)

    Google Scholar 

  66. Wang, H., Zhou, C., Wu, J., Dang, W., Zhu, X., Wang, J.: Deep structure learning for fraud detection. In: IEEE International Conference on Data Mining, pp. 567–576 (2018)

    Google Scholar 

  67. Wang, Y., Wang, L., Yang, J.: Egonet based anomaly detection in e-bank transaction networks. IOP Conf. Ser. Mater. Sci. Eng. 715, 012038 (2020)

    Article  Google Scholar 

  68. West, J., Bhattacharya, M., Islam, R.: Intelligent financial fraud detection practices: an investigation. In: International Conference on Security and Privacy in Communication Networks, pp. 186–203. Springer (2014)

    Google Scholar 

  69. Wu, L., Wu, X., Lu, A., Zhou, Z.H.: A spectral approach to detecting subtle anomalies in graphs. J. Intell. Inf. Syst. 41(2), 313–337 (2013)

    Article  Google Scholar 

  70. Xu, Z., Ke, Y., Wang, Y., Cheng, H., Cheng, J.: A model-based approach to attributed graph clustering. In: Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data, SIGMOD ’12, pp. 505–516. New York, NY, USA, ACM (2012)

    Google Scholar 

  71. Ying, X., Wu, X., Barbará, D.: Spectrum based fraud detection in social networks. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 912–923. IEEE (2011)

    Google Scholar 

  72. Yoon, M., Hooi, B., Shin, K., Faloutsos, C.: Fast and accurate anomaly detection in dynamic graphs with a two-pronged approach. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD ’19, pp. 647–657. Association for Computing Machinery (2019)

    Google Scholar 

  73. Yu, W., Cheng, W., Aggarwal, C..C, Zhang, K., Chen, H., Wang, W.: Netwalk: a flexible deep embedding approach for anomaly detection in dynamic networks. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2672–2681 (2018)

    Google Scholar 

  74. Yuan, S., Wu, X., Li, J., Lu, A.: Spectrum-based deep neural networks for fraud detection. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp. 2419–2422 (2017)

    Google Scholar 

  75. Zhang, S., Zhou, D., Yildirim, M.Y., Alcorn, S., He, J., Davulcu, H., Tong, H.: Hidden: hierarchical dense subgraph detection with application to financial fraud detection. In: Proceedings of the 2017 SIAM International Conference on Data Mining, pp. 570–578 (2017)

    Google Scholar 

  76. Zheng, P., Yuan, S., Wu, X., Li, J., Lu, A.: One-class adversarial nets for fraud detection. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 1286–1293 (2018)

    Google Scholar 

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Acknowledgements

This work was supported by BRD Groupe Societe Generale through Data Science Research Fellowships of 2019.

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Authors and Affiliations

  1. Department of Computer Science, University of Bucharest, Bucharest, Romania

    Paul Irofti & Andrei Pătraşcu

  2. The Research Institute of the University of Bucharest (ICUB), Bucharest, Romania

    Andra Băltoiu

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  1. Paul Irofti
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  2. Andrei Pătraşcu
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  3. Andra Băltoiu
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Correspondence to Paul Irofti .

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  1. Faculty of Computers and Artificial Intelligence, Cairo University, Giza, Egypt

    Aboul-Ella Hassanien

  2. Faculty of Computers and Artificial Intelligence, Cairo University, Giza, Egypt

    Mohamed Hamed N. Taha

  3. Faculty of Computers and Artificial Intelligence, Cairo University, Giza, Egypt

    Nour Eldeen M. Khalifa

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Irofti, P., Pătraşcu, A., Băltoiu, A. (2021). Fraud Detection in Networks. In: Hassanien, AE., Taha, M.H.N., Khalifa, N.E.M. (eds) Enabling AI Applications in Data Science. Studies in Computational Intelligence, vol 911. Springer, Cham. https://doi.org/10.1007/978-3-030-52067-0_23

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