Foreign Trade Risk Warning Model Based on Deep Learning And Association Rule Mining

Article Sidebar

Published: Apr 30, 2025
Keywords:
Foreign Trades Economic Risk Assessment Early Warning System Association Rule Deep Learning

Main Article Content

Xiang Li
Department of Global Food Service Management, Woosuk University, 55338, Jeollabukdo, Korea
Jin Gao*
Department of Global Food Service Management, Woosuk University, 55338, Jeollabukdo, Korea
Wenbo Ma
College of Teacher Education, Jining University, Qufu 273155, Shandong, China

Abstract

An economic risk early warning model identifies and assesses potential financial threats by analysing key economic indicators, market trends, and geopolitical events. Using machine learning algorithms, this model continuously monitors data to detect warning signs, such as shifts in inflation, unemployment, or trade imbalances. Early risk alerts enable policymakers and businesses to proactively address vulnerabilities, mitigate financial impacts, and make informed decisions that strengthen economic resilience against potential downturns. This paper presents a comprehensive analysis of risk assessment and financial prediction within the Chinese finance sector. The study investigates the intricate relationships between various financial indicators and their implications for risk assessment with associative rule mining, financial analysis, and predictive modelling techniques. By examining associative rules, the paper illuminates key patterns and associations, offering valuable insights into the drivers of financial risk. Subsequently, a detailed financial analysis is conducted, highlighting the varying risk profiles among prominent players in the sector based on metrics such as debt-to-equity ratio, profit margin, and market capitalization. Furthermore, predictive modelling results provide insights into the effectiveness of predictive models in forecasting the probability of default for financial companies, aiding stakeholders in making informed risk management decisions. The findings underscore the importance of robust financial health and proactive risk management strategies in navigating the dynamic landscape of the Chinese finance sector. For instance, entities like the Industrial and Commercial Bank of China (ICBC) exhibit a low debt-to-equity ratio of 8.2 and a healthy profit margin of 18%, resulting in a low financial risk level. Conversely, companies like Ping An Bank display higher risk profiles with elevated debt levels, such as a debt-to-equity ratio of 10.2, coupled with lower profit margins, resulting in a high financial risk level. Furthermore, predictive modelling results provide insights into the effectiveness of predictive models in forecasting the probability of default for financial companies. For example, Bank of China (BOC) and the Agricultural Bank of China (ABC) exhibit predicted probabilities of default of 12.1% and 15.5%, respectively, aligning closely with their actual default status.

Article Details

Issue
Vol. 167 No. A2 (S) (2025): Technologies and Their Effects on Real-Time Social Development
Section
Articles

References

Hong, S., Wu, H., Xu, X., & Xiong, W. (2022). Early warning of Foreign Trade financial risk based on decision tree algorithm. Computational Intelligence and Neuroscience, 2022.

Lin, M. (2022). Innovative risk early warning model under data mining approach in risk assessment of internet credit finance. Computational Economics, 59(4), 1443-1464.

Wang, A., & Yu, H. (2022). The construction and empirical analysis of the company’s financial early warning model based on data mining algorithms. Journal of Mathematics, 2022.

Yuan, G., Xie, F., & Tan, H. (2022). Construction of Economic Security Early Warning System Based on Cloud Computing and Data Mining. Computational Intelligence and Neuroscience, 2022.

Venkateswarlu Chandu, Archi Agarwal, Tummala Likhitha, Bindu sri Datla, & Rubi Shagufta. (2024). Prediction Arima Model-based Investor’s Perception On Stock Market Apps. Journal of Computer Allied Intelligence, 2(5).

Zhu, W., Zhang, T., Wu, Y., Li, S., & Li, Z. (2022). Research on optimization of an Foreign Trade financial risk early warning method based on the DS-RF model. International review of financial analysis, 81, 102140.

Xiao, Q., Luo, F., Li, Y., & Pan, D. (2023). Risk prediction and early warning of pilots’ unsafe behaviors using association rule mining and system dynamics. Journal of Air Transport Management, 110, 102422.

Prasanna Kumar, Bomma Durga Nagesh Sri Gupta, Narendra Reddy Chintalacheruvu, Seelam Rama haritha, & Tadikonda Deekshita. (2024). Impact of Interest Rates on the Stock Market with Smart City Environment. Journal of Sensors, IoT & Health Sciences, 2(4), 29-39.

Gao, B. (2022). The use of machine learning combined with data mining technology in financial risk prevention. Computational economics, 59(4), 1385-1405.

Kathikeyan, M., Roy, A., Hameed, S. S., Gedamkar, P. R., Manikandan, G., & Kale, V. (2022, December). Optimization System for Financial Early Warning Model Based on the Computational Intelligence and Neural Network Method. In 2022 5th International Conference on Contemporary Computing and Informatics (IC3I) (pp. 2059-2064). IEEE.

Venkateswarlu Chandu, Molakalapalli Chandana, Tamma manjunatha anudeep, Kandula Venkata Samba Siva Rao, & Tadisetty Vishnu. (2024). Intelligent Designed Assistance Model to Evaluate the Role of Social Media Marketing in Promoting Investment in Mutual Funds in India. Journal of Computer Allied Intelligence, 2(6), 51-64.

Song, Y., & Wu, R. (2022). The impact of financial Foreign Trades’ excessive financialization risk assessment for risk control based on data mining and machine learning. Computational Economics, 60(4), 1245-1267.

P. Brundavani. (2024). A Principal Component Analysis Algorithm for Seed Enterprise Financial Performance and Scientific and Technological Innovation. Journal of Computer Allied Intelligence, 2(2), 49-62.

Wang, Y., & Xue, W. (2022). Sustainable development early warning and financing risk management of resource-based industrial clusters using optimization algorithms. Journal of Foreign Trade Information Management, 35(4/5), 1374-1391.

Liao, S., & Liu, Z. (2022). Foreign Trade financial influencing factors and early warning based on decision tree model. Scientific Programming, 2022, 1-8.

Meng, F., & Li, C. (2022). Safety warning of coal mining face based on big data association rule mining. Journal of Computational Methods in Sciences and Engineering, 22(4), 1035-1052.

Tong, L., & Tong, G. (2022). A novel financial risk early warning strategy based on decision tree algorithm. Scientific Programming, 2022, 1-10.

Kuang, J., Chang, T. C., & Chu, C. W. (2022). Research on financial early warning based on combination forecasting model. Sustainability, 14(19), 12046.

Song, X., Jing, Y., & Qin, X. (2023). BP neural network-based early warning model for financial risk of internet financial companies. Cogent Economics & Finance, 11(1), 2210362.

Wang, G., & Chen, Y. (2022). Enabling Legal Risk Management Model for International Corporation with Deep Learning and Self Data Mining. Computational Intelligence and Neuroscience, 2022.

Du, P., & Shu, H. (2023). Design and Implementation of China Financial Risk Monitoring and Early Warning System Based on Deep Learning. IEEE Access.

Miao, D., Lv, Y., Yu, K., Liu, L., & Jiang, J. (2023). Research on coal mine hidden danger analysis and risk early warning technology based on data mining in China. Process Safety and Environmental Protection, 171, 1-17.

Wan, J., & Yu, B. (2023). Early warning of Foreign Trade financial risk based on improved BP neural network model in low-carbon economy. Frontiers in Energy Research, 10, 1087526.