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

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.