AI-Based Pattern Recognition and Characteristic Analysis of Cross-Border Money Laundering Behaviors in Digital Currency Transactions

Aixin Kang; Xiaowen Ma

doi:10.71222/pw7gxk06

Authors

Aixin Kang Georgetown University, DC, USA Author
Xiaowen Ma University of Rochester, NY, USA Author

DOI:

https://doi.org/10.71222/pw7gxk06

Keywords:

digital currency, money laundering, pattern recognition, artificial intelligence

Abstract

Digital currency transactions have become increasingly prevalent for cross-border money laundering activities, presenting significant challenges to traditional anti-money laundering (AML) frameworks. This research investigates the application of artificial intelligence techniques for identifying and analyzing patterns in cross-border money laundering behaviors within digital currency ecosystems. Through comprehensive analysis of transaction data and behavioral characteristics, this study develops a systematic approach to pattern recognition using machine learning and deep learning methodologies. The research examines various money laundering schemes, including mixing services, layering techniques, and decentralized finance exploitation. Advanced AI algorithms demonstrate superior performance in detecting suspicious transaction patterns compared to conventional rule-based systems. The findings reveal distinct behavioral signatures associated with illicit cross-border activities, enabling more effective detection and prevention strategies. This work contributes to the advancement of regulatory technology solutions and provides insights for policymakers and financial institutions in combating digital currency-based money laundering.

References

1. D. Chowdhury and P. Kulkarni, "Application of data analytics in risk management of fintech companies," in Proc. 2023 Int. Conf. Innovative Data Commun. Technol. Appl. (ICIDCA), 2023, doi: 10.1109/ICIDCA56705.2023.10099795.

2. C. Ju and G. Rao, “Analyzing foreign investment patterns in the US semiconductor value chain using AI-enabled analytics: A framework for economic security,” Pinnacle Acad. Press Proc. Ser., vol. 2, pp. 60–74, 2025.

3. Y. Chen, C. Ni, and H. Wang, "AdaptiveGenBackend: A scalable architecture for low-latency generative AI video processing in content creation platforms," Ann. Appl. Sci., vol. 5, no. 1, 2024.

4. H. Wang et al., “Automated compliance monitoring: A machine learning approach for digital services act adherence in mul-ti-product platforms,” Appl. Comput. Eng., vol. 147, pp. 14–25, 2025.

5. C. Zhu, C. Cheng, and S. Meng, "DRL PricePro: A deep reinforcement learning framework for personalized dynamic pricing in e-commerce platforms with supply constraints," Spectrum Res., vol. 4, no. 1, 2024.

6. Z. Wang et al., “Scientific formula retrieval via tree embeddings,” in Proc. IEEE Int. Conf. Big Data, 2021, doi: 10.1109/BigData52589.2021.9671942.

7. Z. Wang, X. Wang, and H. Wang, "Temporal graph neural networks for money laundering detection in cross-border transac-tions," Acad. Nexus J., vol. 3, no. 2, 2024.

8. J. Wu et al., "Optimizing latency-sensitive AI applications through edge-cloud collaboration," J. Adv. Comput. Syst., vol. 3, no. 3, pp. 19–33, 2023, doi: 10.69987/JACS.2023.30303.

9. Z. Wang et al., “Temporal evolution of sentiment in earnings calls and its relationship with financial performance,” Appl. Comput. Eng., vol. 141, pp. 195–206, 2025.

10. C. Zhu, J. Xin, and D. Zhang, "A deep reinforcement learning approach to dynamic e-commerce pricing under supply chain disruption risk," Ann. Appl. Sci., vol. 5, no. 1, 2024.

11. Y. Zhao et al., “Unit operation combination and flow distribution scheme of water pump station system based on Genetic Algorithm,” Appl. Sci., vol. 13, no. 21, p. 11869, 2023, doi: 10.3390/app132111869.

12. J. Wu et al., “Graph neural networks for efficient clock tree synthesis optimization in complex SoC designs,” Appl. Comput. Eng., vol. 150, pp. 101–111, 2025.

13. G. Wei, X. Wang, and Z. Chu, "Fine-grained action analysis for automated skill assessment and feedback in instructional vid-eos," Pinnacle Acad. Press Proc. Ser., vol. 2, pp. 96–107, 2025.

14. D. Zhang and X. Jiang, "Cognitive collaboration: Understanding human-AI complementarity in supply chain decision pro-cesses," Spectrum Res., vol. 4, no. 1, 2024.

15. Z. Zhang and L. Zhu, "Intelligent detection and defense against adversarial content evasion: A multi-dimensional feature fu-sion approach for security compliance," Spectrum Res., vol. 4, no. 1, 2024.

16. K. Yu et al., "Real-time detection of anomalous trading patterns in financial markets using generative adversarial networks," Preprints, 2025, doi: 10.20944/preprints202504.1591.v1.

17. G. Rao et al., "Jump prediction in systemically important financial institutions' CDS prices," Spectrum Res., vol. 4, no. 2, 2024.

18. H. Wang, K. Qian, C. Ni, and J. Wu, "Distributed batch processing architecture for cross-platform abuse detection at scale," Pinnacle Acad. Press Proc. Ser., vol. 2, pp. 12–27, 2025.

19. C. Ju and T. K. Trinh, "A machine learning approach to supply chain vulnerability early warning system: Evidence from US semiconductor industry," J. Adv. Comput. Syst., vol. 3, no. 11, pp. 21–35, 2023.

20. C. Jiang, H. Wang, and K. Qian, "AI-enhanced cultural resonance framework for player experience optimization in AAA games localization," Pinnacle Acad. Press Proc. Ser., vol. 2, pp. 75–87, 2025.

21. B. Dong and T. K. Trinh, "Real-time early warning of trading behavior anomalies in financial markets: An AI-driven approach," J. Econ. Theory Bus. Manag., vol. 2, no. 2, pp. 14–23, 2025, doi: 10.70393/6a6574626d.323838.

22. L. Yan et al., "Enhanced spatio-temporal attention mechanism for video anomaly event detection," Preprints, 2025, doi: 10.20944/preprints202504.1623.v1.

23. T. K. Trinh and Z. Wang, "Dynamic graph neural networks for multi-level financial fraud detection: A temporal-structural approach," Ann. Appl. Sci., vol. 5, no. 1, 2024.

24. J. Wang, L. Guo, and K. Qian, "LSTM-based heart rate dynamics prediction during aerobic exercise for elderly adults," Preprints, 2025.

25. T. K. Trinh and D. Zhang, "Algorithmic fairness in financial decision-making: Detection and mitigation of bias in credit scoring applications," J. Adv. Comput. Syst., vol. 4, no. 2, pp. 36–49, 2024, doi: 10.69987/JACS.2024.40204.

26. A. A. H. Raji, A. H. F. Alabdoon, and A. Almagtome, "AI in credit scoring and risk assessment: Enhancing lending practices and financial inclusion," in Proc. 2024 Int. Conf. Knowl. Eng. Commun. Syst. (ICKECS), vol. 1, 2024, doi: 10.1109/ICKECS61492.2024.10616493.

27. J.-Y. Shih and Z.-H. Chin, "A fairness approach to mitigating racial bias of credit scoring models by decision tree and the re-weighing fairness algorithm," in Proc. 2023 IEEE 3rd Int. Conf. Electron. Commun., IoT and Big Data (ICEIB), 2023, doi: 10.1109/ICEIB57887.2023.10170339.

28. C. Zhu, J. Xin, and T. K. Trinh, "Data quality challenges and governance frameworks for AI implementation in supply chain management," Pinnacle Acad. Press Proc. Ser., vol. 2, pp. 28–43, 2025.

29. C. Ni et al., "Contrastive time-series visualization techniques for enhancing AI model interpretability in financial risk assess-ment," Preprints, 2025, doi: 10.20944/preprints202504.1984.v1.

30. T. K. Trinh et al., "Behavioral responses to AI financial advisors: Trust dynamics and decision quality among retail investors," Appl. Comput. Eng., vol. 144, pp. 69–79, 2025.

31. Y. Li, X. Jiang, and Y. Wang, "TRAM-FIN: A transformer-based real-time assessment model for financial risk detection in multinational corporate statements," J. Adv. Comput. Syst., vol. 3, no. 9, pp. 54–67, 2023, doi: 10.69987/JACS.2023.30905.

32. D. Zhang and C. Cheng, "AI-enabled product authentication and traceability in global supply chains," J. Adv. Comput. Syst., vol. 3, no. 6, pp. 12–26, 2023, doi: 10.69987/JACS.2023.30602.

33. Z. Zhang and Z. Wu, "Context-aware feature selection for user behavior analytics in zero-trust environments," J. Adv. Comput. Syst., vol. 3, no. 5, pp. 21–33, 2023, doi: 10.69987/JACS.2023.30503.

34. M. Sun, Z. Feng, and P. Li, "Real-time AI-driven attribution modeling for dynamic budget allocation in US e-commerce: A small appliance sector analysis," J. Adv. Comput. Syst., vol. 3, no. 9, pp. 39–53, 2023, doi: 10.69987/JACS.2023.30904.

35. S. Zhang, C. Zhu, and J. Xin, "CloudScale: A lightweight AI framework for predictive supply chain risk management in small and medium manufacturing enterprises," Spectrum Res., vol. 4, no. 2, 2024.

36. S. Zhang, T. Mo, and Z. Zhang, “LightPersML: A lightweight machine learning pipeline architecture for real-time personali-zation in resource-constrained e-commerce businesses,” J. Adv. Comput. Syst., vol. 4, no. 8, pp. 44–56, 2024, doi: 10.69987/JACS.2024.40807.

37. M. Li, W. Liu, and C. Chen, “Adaptive financial literacy enhancement through cloud-based AI content delivery: Effectiveness and engagement metrics,” Ann. Appl. Sci., vol. 5, no. 1, 2024.

38. J. Chen and Z. Lv, “Graph neural networks for critical path prediction and optimization in high-performance ASIC design: A ML-driven physical implementation approach,” in Glob. Conf. Adv. Sci. Technol., vol. 1, no. 1, 2025.

39. S. Zhang, Z. Feng, and B. Dong, “LAMDA: Low-latency anomaly detection architecture for real-time cross-market financial decision support,” Acad. Nexus J., vol. 3, no. 2, 2024.

40. A. Kang, J. Xin, and X. Ma, “Anomalous cross-border capital flow patterns and their implications for national economic security: An empirical analysis,” J. Adv. Comput. Syst., vol. 4, no. 5, pp. 42–54, 2024, doi: 10.69987/JACS.2024.40504.

41. G. Rao, Z. Wang, and J. Liang, “Reinforcement learning for pattern recognition in cross-border financial transaction anomalies: A behavioral economics approach to AML,” Appl. Comput. Eng., vol. 142, pp. 116–127, 2025.

42. J. Liang et al., “Anomaly detection in tax filing documents using natural language processing techniques,” Appl. Comput. Eng., vol. 144, pp. 80–89, 2025.

43. C. Ni, J. Wu, and H. Wang, “Energy-aware edge computing optimization for real-time anomaly detection in IoT networks,” Appl. Comput. Eng., vol. 139, pp. 42–53, 2025.

44. H. McNichols, M. Zhang, and A. Lan, “Algebra error classification with large language models,” in Proc. Int. Conf. Artif. Intell. Educ., Cham: Springer Nature Switzerland, 2023, doi: 10.1007/978-3-031-36272-9_30.

45. M. Zhang, N. Heffernan, and A. Lan, “Modeling and analyzing scorer preferences in short-answer math questions,” arXiv preprint arXiv: 2306.00791, 2023.

46. J. Fan, T. K. Trinh, and H. Zhang, “Deep learning-based transfer pricing anomaly detection and risk alert system for pharma-ceutical companies: A data security-oriented approach,” J. Adv. Comput. Syst., vol. 4, no. 2, pp. 1–14, 2024, doi: 10.69987/JACS.2024.40201.

47. M. Zhang et al., “Automatic short math answer grading via in-context meta-learning,” arXiv preprint arXiv: 2205.15219, 2022.

48. M. Zhang et al., “Math operation embeddings for open-ended solution analysis and feedback,” arXiv preprint arXiv:2104.12047, 2021.

49. D. Qi et al., “Anomaly explanation using metadata,” in Proc. IEEE Winter Conf. Appl. Comput. Vis. (WACV), 2018, doi: 10.1109/WACV.2018.00212.

50. M. Zhang, T. Mathew, and B. Juba, “An improved algorithm for learning to perform exception-tolerant abduction,” in Proc. AAAI Conf. Artif. Intell., vol. 31, no. 1, 2017, doi: 10.1609/aaai.v31i1.10700.

51. S. Yan, “Design of obstacle avoidance system for the blind based on fuzzy control,” Netinfo Security, 2014.

52. K. Mo et al., “Precision kinematic path optimization for high-DOF robotic manipulators utilizing advanced natural language processing models,” in Proc. 5th Int. Conf. Electron. Commun. Artif. Intell. (ICECAI), 2024, doi: 10.1109/ICECAI62591.2024.10675146.

53. K. Mo et al., “Fine-tuning gemma-7b for enhanced sentiment analysis of financial news headlines,” in Proc. IEEE 4th Int. Conf. Electron. Technol., Commun. Inf. (ICETCI), 2024, doi: 10.1109/ICETCI61221.2024.10594605.

54. S. Wu et al., “More is better: Enhancing open-domain dialogue generation via multi-source heterogeneous knowledge,” in Proc. Conf. Empir. Methods Nat. Lang. Process., 2021, doi: 10.18653/v1/2021.emnlp-main.175.

55. S. Wu et al., “Improving the applicability of knowledge-enhanced dialogue generation systems by using heterogeneous knowledge from multiple sources,” in Proc. 15th ACM Int. Conf. Web Search Data Mining, 2022, doi: 10.1145/3488560.3498393.

56. S. Wu et al., “Knowledge-aware dialogue generation via hierarchical infobox accessing and infobox-dialogue interaction graph network,” in IJCAI, 2021.

57. M. Wang et al., “Distilling the documents for relation extraction by topic segmentation,” in Int. Conf. Doc. Anal. Recognit., Cham: Springer, 2021, doi: 10.1007/978-3-030-86549-8_33.

58. M. R. Eatherton et al., “Considering ductility in the design of bare deck and concrete on metal deck diaphragms,” in 17th World Conf. Earthquake Eng., Sendai, Japan.

59. G. Wei et al., “Investigating partial tension field action in gable frame panel zones,” J. Constr. Steel Res., vol. 162, 2019, Art. no. 105746, doi: 10.1016/j.jcsr.2019.105746.

60. G. Wei et al., “Computational study of tension field action in gable frame panel zones,” 2018.

61. H. Foroughi et al., “Seismic demands on steel diaphragms for 3D archetype buildings with concentric braced frames.”

62. G. Wei et al., “Lateral bracing of beams provided by standing seam roof system: concepts and case study,” 2020.

63. H. Foroughi et al., “Seismic response predictions from 3D steel braced frame building simulations.”

64. G. Wei et al., “Seismic design of diaphragms for steel buildings considering diaphragm inelasticity,” J. Struct. Eng., vol. 149, no. 7, 2023, Art. no. 04023077, doi: 10.1061/JSENDH.STENG-11832.

65. L. Zhu, H. Yang, and Z. Yan, “Extracting temporal information from online health communities,” in Proc. 2nd Int. Conf. Crowd Sci. Eng., 2017, doi: 10.1145/3126973.3126975.

66. L. Zhu, H. Yang, and Z. Yan, “Mining medical related temporal information from patients’ self-description,” Int. J. Crowd Sci., vol. 1, no. 2, pp. 110–120, 2017, doi: 10.1108/IJCS-08-2017-0018.