{"id":9688,"date":"2026-08-05T21:55:30","date_gmt":"2026-08-05T13:55:30","guid":{"rendered":"\/jase\/?post_type=tkuisotope&p=9688"},"modified":"2026-08-06T23:11:10","modified_gmt":"2026-08-06T15:11:10","slug":"jase-202611-34-018","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=jase-202611-34-018","title":{"rendered":"Leveraging Big Data for Real-Time Risk Assessment and Management in Cross-Border Business Administration"},"content":{"rendered":"\n
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<\/i> 2026<\/a><\/p>\n<\/div>\n\n\n\n

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<\/i> Volume 34<\/a><\/p>\n<\/div>\n\n\n\n

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Pu Wang and Yao Gu<\/i><\/a><\/p>\n\n\n\n

College of Finance and Economics, Wanjiang University of Technology, Ma\u2019anshan 243000, Anhui, China<\/p>\n<\/div>\n\n\n\n

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Received: March 31, 2026
Accepted: June 02, 2026
Publication Date: August 05, 2026<\/p>\n<\/div>\n<\/div>\n\n\n\n

\"\u4e0a\u50b3\u5716\u7247\"\n\n\n

cGAN based Data Imbalancing Handling for Cross-Border Financial Risk Detection. <\/p>\n<\/div>\n<\/div>\n\n\n\n

<\/i> Copyright <\/strong>The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0)<\/a>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.<\/p>\n\n\n\n

Download Citation:\u00a0 BibTeX <\/a>| http:\/\/dx.doi.org\/10.6180\/jase.202611_34.018<\/a>\u00a0\u00a0<\/p>\n\n\n\n

Download PDF<\/a><\/p>\n\n\n\n

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In globalized and digitally driven world, cross-border businesses face complex financial risks arising from differing regulations, geopolitical tensions, and unstable markets. Traditional risk management models struggle to process large-scale data in real time, limiting effective decision-making. This paper proposes an integrated big data framework for real-time risk analysis in cross-border operations. The framework incorporates Conditional Generative Adversarial Networks (cGANs) to address data imbalance, Long Short-Term Memory (LSTM) networks to forecast evolving risk patterns, and Extreme Value Theory (EVT) to quantify extreme losses through Value-at-Risk (VaR) and Conditional Value-at-Risk (CVaR). Reinforcement Learning (RL) is also included to optimize decision strategies such as hedging and capital allocation. With real-time monitoring and adaptive learning, the system supports proactive and scalable risk management. Experiments using the DataCo Smart Supply Chain Dataset show excellent performance, achieving 99.83% accuracy, 99.77% precision, 99.18% recall, 99.88% F1-score, and strong AUC results, validated further through VaR, CVaR, and Kupiec backtests.<\/p>\n\n\n\n

Keywords: Conditional Generative Adversarial Networks, Extreme Value Theory, Graph Neural Networks, K-Nearest Neighbour and Long Short-Term Memory<\/em><\/p>\n\n\n\n

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