Skip to main content
SpringerLink
Log in

Fraud detection via behavioral sequence embedding

  • Regular Paper
  • Published:
Knowledge and Information Systems Aims and scope Submit manuscript

Abstract

Fraud detection is usually compared to finding a needle in a haystack and remains a challenging task because fraudulent acts are buried in massive amounts of normal behavior and true intentions may be disguised in a single snapshot. Indeed, fraudulent incidents usually take place in consecutive time steps to gain illegal benefits, which provides unique clues for probing fraudulent behavior by considering a complete behavioral sequence rather than detecting fraud from a snapshot of behavior. Additionally, fraudulent behavior may involve different parties, such that the interaction patterns between sources and targets can help distinguish fraudulent acts from normal behavior. Therefore, in this paper, we model the attributed behavioral sequences generated from consecutive behaviors in order to capture the sequential patterns, while those that deviate from the pattern can be detected as fraudulence. Considering the characteristics of the behavioral sequence, we propose a novel model, NHA-LSTM, by augmenting the traditional LSTM with a modified forget gate, where the interval time between consecutive time steps is considered. Furthermore, we design a self-historical attention mechanism to allow for long time dependencies, which can help identify repeated or cyclical appearances. In addition, we propose an enhanced network embedding method, FraudWalk, to construct embeddings for the nodes in the interaction network with regard to higher-order interactions and particular time constraints for revealing potential group fraudulence. The node embeddings, along with the feature vectors, are fed into the model to capture the interactions between sources and targets. To validate the effectiveness of sequential behavior embeddings, we experiment on a real-world telecommunication dataset with prediction and classification tasks based on the learned embeddings. The experimental results show that the learned embeddings can better identify fraudulent behavior. Finally, we visualize the weights of the attention mechanism to provide a rational interpretation of human behavioral patterns.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Perozzi B, Al-Rfou R, Skiena S (2014) Deepwalk: online learning of social representations. In: SIGKDD. ACM, New York, NY, USA, pp 701–710

  2. Phua C, Lee V, Smith K, Gayler R (2010) A comprehensive survey of data mining-based fraud detection research. CoRR, vol. abs/1009.6119

  3. Abdallah A, Maarof MA, Zainal A (2016) Fraud detection system: a survey. J Netw Comput Appl 68:90–113

    Article  Google Scholar 

  4. Ramaswamy S, Rastogi R, Shim K (2000) Efficient algorithms for mining outliers from large data sets. In: Proceedings of the 2000 ACM SIGMOD international conference on management of data, pp 427–438

  5. Yamanishi K, Takeuchi JI, Williams G, Milne P (2004) On-line unsupervised outlier detection using finite mixtures with discounting learning algorithms. Data Min Knowl Discov 8(3):275–300

    Article  MathSciNet  Google Scholar 

  6. Hooi B, Shin K, Song HA, Beutel A, Shah N, Faloutsos C (2017) Graph-based fraud detection in the face of camouflage. ACM Trans Knowl Discov Data 11(4):1–26

    Article  Google Scholar 

  7. Robinson WN, Aria A (2018) Sequential fraud detection for prepaid cards using hidden markov model divergence. Expert Syst Appl 91:235–251

    Article  Google Scholar 

  8. Bhattacharyya S, Jha S, Tharakunnel K, Westland JC (2011) Data mining for credit card fraud: a comparative study. Decis Support Syst 50(3):602–613

    Article  Google Scholar 

  9. Tsang S, Koh YS, Dobbie G, Alam S (2014) Detecting online auction shilling frauds using supervised learning. Expert Syst Appl 41(6):3027–3040

    Article  Google Scholar 

  10. Lipton ZC, Berkowitz J, Elkan C (2015) A critical review of recurrent neural networks for sequence learning. CoRR, vol. abs/1506.00019

  11. Sutskever I, Vinyals O, Le QV (2014) Sequence to sequence learning with neural networks. CoRR, vol. abs/1409.3215

  12. Li X, Zhao B, Lu X (2017) Mam-rnn: multi-level attention model based rnn for video captioning. In: Proceedings of the twenty-sixth international joint conference on artificial intelligence, pp 2208–2214

  13. Zhai S, Chang Kh, Zhang R, Zhang ZM (2016) Deepintent: learning attentions for online advertising with recurrent neural networks. In: Proceedings of the 22Nd ACM SIGKDD international conference on knowledge discovery and data mining, pp 1295–1304

  14. Cho K, Van Merrienboer B, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, Bengio Y (2014) Learning phrase representations using rnn encoder-decoder for statistical machine translation. CoRR, vol. abs/1406.1078

  15. Collins J, Sohl-Dickstein J, Sussillo D (2016) Capacity and trainability in recurrent neural networks. CoRR, vol. abs/1611.09913

  16. Neil D, Pfeiffer M, Liu SC (2016) Phased lstm: Accelerating recurrent network training for long or event-based sequences. CoRR, vol. abs/1610.09513

  17. Liu Q, Wu S, Wang L, Tan T (2016) Predicting the next location: a recurrent model with spatial and temporal contexts. In: Proceedings of the thirtieth AAAI conference on artificial intelligence, pp 194–200

  18. Bahdanau D, Cho K, Bengio Y (2014) Neural machine translation by jointly learning to align and translate. CoRR, vol. abs/1409.0473

  19. Chorowski J, Bahdanau D, Serdyuk D, Cho K, Bengio Y (2015) Attention-based models for speech recognition. In: Proceedings of the 28th international conference on neural information processing systems, pp 577–585

  20. Chen J, Zhang H, He X, Nie L, Liu W, Chua TS (2017) Attentive collaborative filtering: multimedia recommendation with item- and component-level attention. In: Proceedings of the 40th international ACM SIGIR conference on research and development in information retrieval, pp 335–344

  21. Feng J, Li Y, Zhang C, Sun F, Meng F, Guo A, Jin D (2018) Deepmove: predicting human mobility with attentional recurrent networks. In: Proceedings of the 2018 world wide web conference, pp 1459–1468

  22. Wang Y, Shen H, Liu S, Gao J, Cheng X (2017) Cascade dynamics modeling with attention-based recurrent neural network. In: Proceedings of the twenty-sixth international joint conference on artificial intelligence, pp 2985–2991

  23. Cai H, Zheng VW, Chang KC (2017) A comprehensive survey of graph embedding: problems, techniques and applications. CoRR, vol. abs/1709.07604

  24. Kruskal JB, Wish M (1978) Multidimensional scaling. CRC Press, Boca Raton

    Book  Google Scholar 

  25. Roweis ST, Saul LK (2000) Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500):2323–2326

    Article  Google Scholar 

  26. Tenenbaum JB, Silva Vd, Langford JC (2000) A global geometric framework for nonlinear dimensionality reduction. Science 290(5500):2319–2323

    Article  Google Scholar 

  27. Belkin M, Niyogi P (2001) Laplacian eigenmaps and spectral techniques for embedding and clustering. In: NIPS. MIT Press, Cambridge, MA, USA, pp 585–591

  28. Mikolov T, Chen K, Corrado G, Dean J (2013) Efficient estimation of word representations in vector space. CoRR, vol. abs/1301.3781

  29. Grover A, Leskovec J (2016) Node2vec: scalable feature learning for networks. In: SIGKDD. ACM, New York, NY, USA, pp 855–864

  30. Tang J, Qu M, Wang M, Zhang M, Yan J, Mei Q (2015) Line: large-scale information network embedding. In: WWW. Republic and canton of Geneva, Switzerland: international world wide web conferences steering committee, pp 1067–1077

  31. Wang D, Cui P, Zhu W (2016) Structural deep network embedding. In: SIGKDD. ACM, New York, NY, USA, pp 1225–1234

  32. Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780

    Article  Google Scholar 

  33. Guo J, Liu G, Zuo Y, Wu J (Nov 2018) Learning sequential behavior representations for fraud detection. In: 2018 IEEE international conference on data mining (ICDM), pp 127–136

  34. Ma F, Chitta R, Zhou J, You Q, Sun T, Gao J (2017) Dipole: diagnosis prediction in healthcare via attention-based bidirectional recurrent neural networks. In: Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining, pp 1903–1911

  35. Alterovitz G, Ramoni MF (2006) Discovering biological guilds through topological abstraction. In: AMIA annual symposium proceedings, vol 2006, p 1. American medical informatics association

  36. Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. CoRR, vol. abs/1412.6980

  37. de Boer P-T, Kroese DP, Mannor S, Rubinstein RY (2005) A tutorial on the cross-entropy method. Ann Oper Res 134(1):19–67

    Article  MathSciNet  Google Scholar 

  38. He H, Garcia EA (2009) Learning from imbalanced data. IEEE Trans Knowl Data Eng 21(9):1263–1284

    Article  Google Scholar 

  39. Wu G, Chang EY (2005) Kba: kernel boundary alignment considering imbalanced data distribution. IEEE Trans Knowl Data Eng 17(6):786–795

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Economics and Management, Beihang University, Beijing, China

    Guannan Liu, Yuan Zuo, Junjie Wu & Ren-yong Guo

  2. School of Computing, National University of Singapore, Singapore, Singapore

    Jia Guo

  3. Beijing Advanced Innovation Center for Big Data and Brain Computing, Beihang University, Beijing, 100191, China

    Junjie Wu

  4. Beijing Key Laboratory of Emergency Support Simulation Technologies for City Operations, Beihang University, Beijing, 100191, China

    Junjie Wu

Authors
  1. Guannan Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jia Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yuan Zuo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Junjie Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ren-yong Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yuan Zuo.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Dr. Liu was supported by National Science Foundation of China (NSFC) under Grant 71701007. Dr. Wu was supported by the National Key R&D Program of China (2019YFB2101804) and NSFC under Grants 71725002, 71531001, U1636210, 71490723. Dr. Zuo was supported by NSFC under Grant 71901012 and China Postdoctoral Science Foundation 2018M640045.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, G., Guo, J., Zuo, Y. et al. Fraud detection via behavioral sequence embedding. Knowl Inf Syst 62, 2685–2708 (2020). https://doi.org/10.1007/s10115-019-01433-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10115-019-01433-3

Keywords

Search

Navigation