BiLSTM based Temporal Modeling for Financial Risk Forecasting using Multivariate Time Series Data

Financial risk warning has become an essential aspect of modern economic management, especially in an era marked by volatile markets, growing data complexity, and unpredictable economic shifts. Conventional approaches based on statistical analysis often fall short in identifying hidden patterns, temporal dependencies, and nonlinear relationships in financial data. To overcome these limitations, this study proposes an intelligent financial risk warning system that utilizes advanced data driven modeling and dynamic learning mechanisms. The proposed system analyzes key financial indicators such as cash flow, debt ratios, liquidity, profitability, and operational metrics to identify early signs of financial instability. By capturing temporal trends and interdependencies across these variables, the system delivers timely and accurate warning signals, enabling proactive risk management. Experimental results based on benchmark financial datasets reveal significant improvements in accuracy, reliability, and responsiveness compared to traditional techniques. The system achieved an accuracy rate of 97.6%, demonstrating its effectiveness in real-world financial risk scenarios. This research contributes a scalable, intelligent, and automated solution for enhancing financial decision-making, supporting institutions, investors, and regulatory bodies in mitigating potential economic risks.