Journal: Modern Economics & Management Forum DOI: 10.32629/memf.v7i4.5451
Abstract
With the rapid expansion of Chinese enterprises' overseas investment and cross-border financing, cross-border capital movements have become increasingly complex, involving multiple entities, jurisdictions, transaction structures and financial channels. Although China has established a relatively comprehensive regulatory framework covering Outward Direct Investment (ODI) management, foreign exchange supervision, customer due diligence and Anti-Money Laundering (AML) monitoring, fragmented multi-source data continue to constrain integrated fund behavior analysis and dynamic risk identification. From a data-driven risk management perspective, this study proposes a multi-source data fusion-driven framework for cross-border fund behavior identification. By integrating entity information, transaction records, ownership relationships, fund flow paths, relationship networks, and external risk factors, the framework constructs dynamic fund behavior profiles based on the analytical dimensions of entity–relationship–fund flow–time and identifies potential deviations between actual fund activities and expected business purposes. The study extends cross-border financial risk research from traditional entity-based assessment to fund behavior analysis, providing a data-driven analytical framework for continuous monitoring, risk early warning, and digital risk governance in cross-border investment and financing activities.
Keywords
cross-border investment and financing; outward direct investment (ODI); financial risk governance; digital risk management
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[1] XU H, MIAO W, ZHANG S. Cross-border capital inflows and systemic financial risks[J]. International Review of Economics & Finance, 2025, 99: 104049.
[2] Buckley P.J., Clegg L.J., Cross A.R., et al. The determinants of Chinese outward foreign direct investment[J]. Journal of International Business Studies, 2007.
[3] Kolstad I., Wiig A. What determines Chinese outward FDI?[J]. Journal of World Business, 2012.
[4] Chen Z., Khoa L.D.V., Teoh E.N., et al. Machine learning techniques for anti-money laundering (AML) solutions in suspicious transaction detection: a review[J]. Knowledge and Information Systems, 2018.
[5] Jullum M., Løland A., Huseby R.B., et al. Detecting money laundering transactions with machine learning[J]. Journal of Money Laundering Control, 2020.
[6] Savage, D., Wang, Q., Chou, P., Zhang, X., & Yu, X. (2017). Detection of money laundering groups: Supervised learning on small networks. In AAAI Workshop on AI for Financial Services (pp. 1–6). AAAI Press.
[7] Colladon, A. F., & Remondi, E. (2017). Using social network analysis to prevent money laundering. Expert Systems with Applications, 67, 49–58.
[8] Deprez B., Vanderschueren T., Baesens B., et al. Network Analytics for Anti-money Laundering—A Systematic Literature Review and Experimental Evaluation[J]. INFORMS Journal on Data Science, 2025.
[9] WANG G, CHEN G, ZHAO H M, et al. Leveraging multisource heterogeneous data for financial risk prediction: A novel hybrid-strategy-based self-adaptive method[J]. MIS Quarterly, 2021, 45(4): 1949-1998.
[2] Buckley P.J., Clegg L.J., Cross A.R., et al. The determinants of Chinese outward foreign direct investment[J]. Journal of International Business Studies, 2007.
[3] Kolstad I., Wiig A. What determines Chinese outward FDI?[J]. Journal of World Business, 2012.
[4] Chen Z., Khoa L.D.V., Teoh E.N., et al. Machine learning techniques for anti-money laundering (AML) solutions in suspicious transaction detection: a review[J]. Knowledge and Information Systems, 2018.
[5] Jullum M., Løland A., Huseby R.B., et al. Detecting money laundering transactions with machine learning[J]. Journal of Money Laundering Control, 2020.
[6] Savage, D., Wang, Q., Chou, P., Zhang, X., & Yu, X. (2017). Detection of money laundering groups: Supervised learning on small networks. In AAAI Workshop on AI for Financial Services (pp. 1–6). AAAI Press.
[7] Colladon, A. F., & Remondi, E. (2017). Using social network analysis to prevent money laundering. Expert Systems with Applications, 67, 49–58.
[8] Deprez B., Vanderschueren T., Baesens B., et al. Network Analytics for Anti-money Laundering—A Systematic Literature Review and Experimental Evaluation[J]. INFORMS Journal on Data Science, 2025.
[9] WANG G, CHEN G, ZHAO H M, et al. Leveraging multisource heterogeneous data for financial risk prediction: A novel hybrid-strategy-based self-adaptive method[J]. MIS Quarterly, 2021, 45(4): 1949-1998.
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