Skip to main content
SpringerLink
Log in

A Novel Spatiotemporal Prediction Approach Based on Graph Convolution Neural Networks and Long Short-Term Memory for Money Laundering Fraud

  • Research Article-Computer Engineering and Computer Science
  • Published:
Arabian Journal for Science and Engineering Aims and scope Submit manuscript

Abstract

Money laundering is an act of criminals attempting to cover up the nature and source of their illegal gains. Large-scale money laundering has a great harm to a country’s economy, political order and even social stability. Therefore, it is essential to predict the risk of money laundering scientifically and reasonably. Money laundering data have complex temporal dependency. Historical transactions have an impact on current transactions. Different transactions also have complex spatial correlation. For this very reason, a hybrid spatiotemporal money laundering prediction model based on graph convolution neural networks (GCN) and long short-term memory (LSTM), abbreviated MGC-LSTM, is proposed to learn the dependency between different money laundering transactions. Firstly, LSTM is employed to obtain the temporal dependence of money laundering data set at different times; secondly, GCN is wielded to learn the complex spatial dependency of different money laundering transactions. Historical observations on different transactions, temporal and transactions features are defined as graph signals. For each time stamp, the results trained by LSTM are served as the input of GCN; finally, we compare the MGC-LSTM with other state-of-the-art algorithms to evaluate the performance of the proposed method. The experimental results demonstrate that MGC-LSTM outperforms other comparing algorithms with respect to effectiveness and significance.

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
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  1. Drezewski, R.; Sepielak, J.; Filipkowski, W.: The application of social network analysis algorithms in a system supporting money laundering detection. Inf. Sci. Ny 295, 18–32 (2015). Doi: https://doi.org/10.1016/j.ins.2014.10.015

    Article  MathSciNet  Google Scholar 

  2. Barone, R.; Masciandaro, D.: Cryptocurrency or usury? Crime and alternative money laundering techniques. Eur. J. Law Econ 47, 233–254 (2019). Doi: https://doi.org/10.1007/s10657-019-09609-6

    Article  Google Scholar 

  3. Weber, M.; Chen, J.; Suzumura, T. et al.: Scalable graph learning for anti-money laundering: a first look. 295, 18–32 (2018). arXiv preprint: arXiv:1812.00076

  4. Campbell-Verduyn, M.: Bitcoin, crypto-coins, and global anti-money laundering governance. Crime Law Soc. Chang. 69, 283–305 (2018). Doi: https://doi.org/10.1007/s10611-017-9756-5

    Article  Google Scholar 

  5. Xia, P.; Ni, Z.; Zhu, X., et al.: A novel prediction method based on improved binary glowworm swarm optimization and multi-fractal dimension for P2P lending investment risk. IEEE Access 8, 23232–23245 (2020). Doi: https://doi.org/10.1109/ACCESS.2020.2970482

    Article  Google Scholar 

  6. Zhang, B.; Tan, R.; Lin, C.J.: Forecasting of e-commerce transaction volume using a hybrid of extreme learning machine and improved moth-flame optimization algorithm. Appl. Intell. (2020). Doi: https://doi.org/10.1007/s10489-020-01840-y

    Article  Google Scholar 

  7. Zhang, X.; Jiang, H.: Application of copula function in financial risk analysis. Comput. Electr. Eng. 77, 376–388 (2019). Doi: https://doi.org/10.1016/j.compeleceng.2019.06.011

    Article  Google Scholar 

  8. Ali, M.A.; Azad, M.A.; Parreno Centeno, M., et al.: Consumer-facing technology fraud: economics, attack methods and potential solutions. Future Gener. Comput. Syst. 100, 408–427 (2019). Doi: https://doi.org/10.1016/j.future.2019.03.041

    Article  Google Scholar 

  9. Vanhoeyveld, J.; Martens, D.; Peeters, B.: Value-added tax fraud detection with scalable anomaly detection techniques. Appl. Soft. Comput. J. 86, 105895 (2020). Doi: https://doi.org/10.1016/j.asoc.2019.105895

  10. Singh, K.; Best, P.: Anti-money laundering: using data visualization to identify suspicious activity. Int J Account Inf Syst 34, 100418 (2019). Doi: https://doi.org/10.1016/j.accinf.2019.06.001

    Article  Google Scholar 

  11. Weber, M.; Domeniconi, G.; Chen, J. et al.: Anti-money laundering in bitcoin: experimenting with graph convolutional networks for financial forensics (2019). arXiv preprint: arXiv:1908.02591

  12. Gao, S.; Xu, D.: Conceptual modeling and development of an intelligent agent-assisted decision support system for anti-money laundering. Exp. Syst. Appl. 36, 1493–1504 (2009). Doi: https://doi.org/10.1016/j.eswa.2007.11.059

    Article  Google Scholar 

  13. Fronzetti Colladon, A.; Remondi, E.: Using social network analysis to prevent money laundering. Exp. Syst. Appl. 67, 49–58 (2017). Doi: https://doi.org/10.1016/j.eswa.2016.09.029

    Article  Google Scholar 

  14. Demetis, D.S.: Fighting money laundering with technology: A case study of Bank X in the UK. Decis. Support Syst. 105, 96–107 (2018). Doi: https://doi.org/10.1016/j.dss.2017.11.005

    Article  Google Scholar 

  15. Nasir, M.; Gazder, U.; Maslehuddin, M., et al.: Prediction of properties of concrete cured under hot weather using multivariate regression and ANN models. Arab. J. Sci. Eng. 45, 4111–4123 (2020). Doi: https://doi.org/10.1007/s13369-020-04403-y

    Article  Google Scholar 

  16. Du, Y.W.; Wang, S.S.; Wang, Y.M.: Group fuzzy comprehensive evaluation method under ignorance. Expert Syst Appl 126, 92–111 (2019). Doi: https://doi.org/10.1016/j.eswa.2019.02.006

    Article  Google Scholar 

  17. Su, Y.; Guo, N.; Tian, Y.; Zhang, X.: A non-revisiting genetic algorithm based on a novel binary space partition tree. Inf. Sci. Ny 512, 661–674 (2020). Doi: https://doi.org/10.1016/j.ins.2019.10.016

    Article  Google Scholar 

  18. Ding, Y.; Shi, Y.; Wang, A. et al.: Block-oriented correlation power analysis with bitwise linear leakage: An artificial intelligence approach based on genetic algorithms. Future Gener. Comput. Syst. 106:34–42 (2020). Doi: https://doi.org/10.1016/j.future.2019.12.046

  19. Ouyed, O.; Allili, M.S.: Feature weighting for multinomial kernel logistic regression and application to action recognition. Neurocomputing 275, 1752–1768 (2018). Doi: https://doi.org/10.1016/j.neucom.2017.10.024

    Article  Google Scholar 

  20. Chen, W.J.; Shao, Y.H.; Li, C.N., et al.: Ν-projection twin support vector machine for pattern classification. Neurocomputing 376, 10–24 (2020). Doi: https://doi.org/10.1016/j.neucom.2019.09.069

    Article  Google Scholar 

  21. Ping, Z.J.; Fei, G.P.; Fang, F.: An ATPSO-BP neural network modeling and its application in mechanical property prediction. Comput. Mater Sci. 163, 262–266 (2019). Doi: 10.1016/j.commatsci.2019.03.037

  22. Mohammadi, M.; Al-Fuqaha, A.; Sorour, S. et al.: Deep Learning for IoT big data and streaming analytics: a survey. IEEE Commun. Surv. Tut. 20(4): 2923–2960 (2018). Doi: https://doi.org/10.1109/COMST.2018.2844341

  23. Yang, Z.; Mourshed, M.; Liu, K., et al.: A novel competitive swarm optimized RBF neural network model for short-term solar power generation forecasting. Neurocomputing 397, 415–421 (2020). Doi: https://doi.org/10.1016/j.neucom.2019.09.110

    Article  Google Scholar 

  24. Grachev, A.M.; Ignatov, D.I.; Savchenko, A.V.: Compression of recurrent neural networks for efficient language modeling. Appl. Soft. Comput. J. 79, 354–362 (2019). Doi: https://doi.org/10.1016/j.asoc.2019.03.057

    Article  Google Scholar 

  25. Wen, L.; Zhang, X.; Bai, H.; Xu, Z.: Structured pruning of recurrent neural networks through neuron selection. Neural Netw. 123, 134–141 (2020). Doi: https://doi.org/10.1016/j.neunet.2019.11.018

    Article  Google Scholar 

  26. Zhang, Z.; Ye, L.; Qin, H., et al.: Wind speed prediction method using shared weight long short-term memory network and Gaussian process regression. Appl. Energy 247, 270–284 (2019). Doi: https://doi.org/10.1016/j.apenergy.2019.04.047

    Article  Google Scholar 

  27. Zhang, B.; Li, J.; Quan, L., et al.: Sequence-based prediction of protein-protein interaction sites by simplified long short-term memory network. Neurocomputing 357, 86–100 (2019). Doi: https://doi.org/10.1016/j.neucom.2019.05.013

    Article  Google Scholar 

  28. Ashour, A.S.; El-Attar, A.; Dey, N., et al.: Long short term memory based patient-dependent model for FOG detection in Parkinson’s disease. Pattern Recognit Lett. 131, 23–29 (2020). Doi: https://doi.org/10.1016/j.patrec.2019.11.036

    Article  Google Scholar 

  29. Giménez, M.; Palanca, J.; Botti, V.: Semantic-based padding in convolutional neural networks for improving the performance in natural language processing. A case of study in sentiment analysis. Neurocomputing 378, 315–323 (2020). Doi: https://doi.org/10.1016/j.neucom.2019.08.096

    Article  Google Scholar 

  30. Liu, Z.T.; Li, S.H.; Wu, M., et al.: Eye localization based on weight binarization cascade convolution neural network. Neurocomputing 378, 45–53 (2020). Doi: https://doi.org/10.1016/j.neucom.2019.10.048

    Article  Google Scholar 

  31. Sarıgül, M.; Ozyildirim, B.M.; Avci, M.: Differential convolutional neural network. Neural Netw. 116, 279–287 (2019). Doi: https://doi.org/10.1016/j.neunet.2019.04.025

    Article  Google Scholar 

  32. Kipf, T.N.; Welling, M. Semi-supervised classification with graph convolutional networks. In: 5th International Conference on Learn Represent ICLR 2017—Conference on Track Proceedings, pp 1–14 (2017)

  33. Gikonyo, C.: Rationalising the use of the anti-money laundering regime in tackling Somalia’s piracy for ransoms. Int. J. Law Crime Justice 52, 155–164 (2018). Doi: https://doi.org/10.1016/j.ijlcj.2017.11.004

  34. Fröwis, M.; Gottschalk, T.; Haslhofer, B., et al.: Safeguarding the evidential value of forensic cryptocurrency investigations. Forensic. Sci. Int. Digit. Investig. (2020). Doi: https://doi.org/10.1016/j.fsidi.2019.200902

    Article  Google Scholar 

  35. Loayza, N.; Villa, E.; Misas, M.: Illicit activity and money laundering from an economic growth perspective: A model and an application to Colombia. J. Econ. Behav. Organ. 159, 442–487 (2019). Doi: https://doi.org/10.1016/j.jebo.2017.10.002

    Article  Google Scholar 

  36. Zhao, L.S.: Underground banks in NYC, their main clientele and operators: the perspective of Chinese illegal immigrants. Int. J. Law Crime Justice 41, 36–57 (2013). Doi: https://doi.org/10.1016/j.ijlcj.2012.11.003

    Article  Google Scholar 

  37. Ravenda, D.; Valencia-Silva, M.M.; Argiles-Bosch, J.M.; García-Blandón, J.: Money laundering through the strategic management of accounting transactions. Crit. Perspect Account 60, 65–85 (2019). Doi: https://doi.org/10.1016/j.cpa.2018.08.003

    Article  Google Scholar 

  38. Isa, Y.M.; Sanusi, Z.M.; Haniff, M.N.; Barnes, P.A. (2015) Money laundering risk: from the bankers’ and regulators perspectives. Procedia Econ. Financ. 28, 7–13. Doi: https://doi.org/10.1016/s2212-5671(15)01075-8

  39. Vandezande, N.: Virtual currencies under EU anti-money laundering law. Comput. Law Secur. Rev. 33, 341–353 (2017). Doi: https://doi.org/10.1016/j.clsr.2017.03.011

    Article  Google Scholar 

  40. Blume, L.; Easley, D.; O’hara, M.: Market statistics and technical analysis: the role of volume. J. Financ. 49(1), 153–181 (1994). Doi: https://doi.org/10.1111/j.1540-6261.1994.tb04424.x

    Article  Google Scholar 

  41. Taylor, M.P.; Allen, H.: The use of technical analysis in the foreign exchange market. J. Int. Money Financ. 11(3), 304–314 (1992). Doi: https://doi.org/10.1016/0261-5606(92)90048-3

    Article  Google Scholar 

  42. Han, J.; Barman, U.;, Hayes, J. et al.: NextGen AML: Distributed deep learning based language technologies to augment anti money laundering investigation. In: ACL 2018—56th Annual Meeting of Association in Computing Linguist Proceedings of the System Demonstrator, pp. 37–42 (2015). Doi: https://doi.org/10.18653/v1/p18-4007

  43. Troiano, L.; Villa, E.M.; Loia, V.: Replicating a trading strategy by means of LSTM for financial industry applications. IEEE Trans. Industr. Inf. 14(7), 3226–3234 (2018). Doi: https://doi.org/10.1109/TII.2018.2811377

    Article  Google Scholar 

  44. Fischer, T.; Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. Eur. J. Oper. Res. 270(2), 654–669 (2018). Doi: https://doi.org/10.1016/j.ejor.2017.11.054

    Article  MathSciNet  MATH  Google Scholar 

  45. Yan, B.; Aasma, M.: A novel deep learning framework: prediction and analysis of financial time series using CEEMD and LSTM. Expert Syst. Appl. 159, 113609 (2020). Doi: https://doi.org/10.1016/j.eswa.2020.113609

    Article  Google Scholar 

  46. Heryadi, Y.; Warnars, H.L.H.S. Learning temporal representation of transaction amount for fraudulent transaction recognition using CNN, Stacked LSTM, and CNN-LSTM. 2017 IEEE International Conference on Cybernatics Computing Intelligence Cybernatics 2017—Proceedings 2017, pp 84–89 (2018). Doi: https://doi.org/10.1109/CYBERNETICSCOM.2017.8311689

  47. Zhang, Y.; Yan, B.; Aasma, M.: A novel deep learning framework: Prediction and analysis of financial time series using CEEMD and LSTM. Expert Syst Appl 159, 113609 (2020). Doi: https://doi.org/10.1016/j.eswa.2020.113609

    Article  Google Scholar 

  48. Jiang, J.; Chen, J.; Gu, T. et al.: Anomaly Detection with Graph Convolutional Networks for Insider Threat and Fraud Detection. Proc - IEEE Mil Commun Conf MILCOM 2019, pp. 109–114. Doi: https://doi.org/10.1109/MILCOM47813.2019.9020760 (2019)

  49. Qi, Y.; Li, Q.; Karimian, H.; Liu, D.: A hybrid model for spatiotemporal forecasting of PM 2.5 based on graph convolutional neural network and long short-term memory. Sci Total Environ 664, 1–10 (2019). Doi: https://doi.org/10.1016/j.scitotenv.2019.01.333

    Article  Google Scholar 

  50. Defferrard, M,; Bresson, X,; Vandergheynst, P.: Convolutional neural networks on graphs with fast localized spectral filtering (2016). arXiv preprint: arXiv:1606.09375

  51. Rhee, S,; Seo, S,; Kim, S.: Hybrid approach of relation network and localized graph convolutional filtering for breast cancer subtype classification (2017). arXiv preprint: arXiv:1711.05859

  52. Hammond, D.K.; Vandergheynst, P.; Gribonval, R.: Wavelets on graphs via spectral graph theory. Appl. Comput. Harmon. Anal. 30(2), 129–150 (2011). Doi: https://doi.org/10.1016/j.acha.2010.04.005

    Article  MathSciNet  MATH  Google Scholar 

  53. Michaël, D.; Bresson, X.; Vandergheynst, P.: Convolutional neural networks on graphs with fast localized spectral filtering. Adv. Neural. Inf. Process. Syst. 395–398,(2016). Doi: https://doi.org/10.1016/j.commatsci.2018.05.018

  54. Hochreiter, S.; Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997). Doi: https://doi.org/10.1162/neco.1997.9.8.1735

    Article  Google Scholar 

  55. Bera, S.; Shrivastava, V.K.: Analysis of various optimizers on deep convolutional neural network model in the application of hyperspectral remote sensing image classification. Int. J. Remote Sens. 41(7), 2664–2683 (2020). Doi: https://doi.org/10.1080/01431161.2019.1694725

    Article  Google Scholar 

  56. Kingma, D.P,; Ba, J.: Adam: A method for stochastic optimization (2014). arXiv preprint: arXiv:1412.6980.

  57. Balaji, E.; Brindha, D.; Elumalai, V.K., et al.: Automatic and non-invasive Parkinson’s disease diagnosis and severity rating using LSTM network. Appl. Soft Comput. 108, 107463 (2021). Doi: https://doi.org/10.1016/j.asoc.2021.107463

    Article  Google Scholar 

  58. Jozefowicz, R,; Zaremba, W,; Sutskever, I.: An empirical exploration of recurrent network architectures. In Proceedings of the 32nd International Conference on Machine Learning. PMLR, 37, 2342–2350 (2015).

  59. Yang, S.; Yu, X.; Zhou, Y.: LSTM and GRU neural network performance comparison study: taking Yelp review dataset as an example. In: 2020 International Workshop on Electronic Communication and Artificial Intelligence (IWECAI). IEEE, pp. 98–101 (2020). Doi: https://doi.org/10.1109/IWECAI50956.2020.00027

  60. Alarab, I,; Prakoonwit, S,; Nacer, M.I.: Comparative analysis using supervised learning methods for anti-money laundering in bitcoin. In: Proceedings of the 2020 5th International Conference on Machine Learning Technologies, pp. 1–17 (2020). Doi: https://doi.org/10.1145/3409073.3409078

  61. Chen, M.R.; Chen, B.P.; Zeng, G.Q.: An adaptive fractional order BP neural network based on extremal optimization for handwritten digits recognition. Neurocomputing 391, 260–272 (2020). Doi: https://doi.org/10.1016/j.neucom.2018.10.090

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the Anhui Provincial Natural Science Foundation under Grant No. 1908085QG298, and 1908085MG232, the National Nature Science Foundation of China under Grant No. 91546108, and No. 71490725, the Anhui Provincial Science and Technology Major Projects Grant 201903a05020020, the Fundamental Research Funds for the Central Universities under grant No. JZ2019HGTA0053, No. JZ2019 HGBZ0128, and the Open Research Fund Program of Key Laboratory of Process Optimization and Intelligent Decision-making, Ministry of Education. The authors would like to thank the reviewers for their comments and suggestions.

Author information

Authors and Affiliations

  1. School of Management, Hefei University of Technology, Hefei, 230009, China

    Pingfan Xia, Zhiwei Ni, Xuhui Zhu & Peng Peng

  2. Key Laboratory of Process Optimization and Intelligent Decision-Making, Ministry of Education, Hefei, 230009, China

    Pingfan Xia, Zhiwei Ni, Xuhui Zhu & Peng Peng

  3. Department of Computer Science and Software Engineering, Swinburne University of Technology, Melbourne, 3122, Australia

    Hongwang Xiao

Authors
  1. Pingfan Xia
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhiwei Ni
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hongwang Xiao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xuhui Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Peng Peng
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhiwei Ni.

Ethics declarations

Conflicts of interest

The authors declare no conflict of interest.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xia, P., Ni, Z., Xiao, H. et al. A Novel Spatiotemporal Prediction Approach Based on Graph Convolution Neural Networks and Long Short-Term Memory for Money Laundering Fraud. Arab J Sci Eng 47, 1921–1937 (2022). https://doi.org/10.1007/s13369-021-06116-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13369-021-06116-2

Keywords

Search

Navigation