Enterprise financial risk prediction and intelligent early warning model based on deep learning

Enterprise financial risk prediction is a critical component in ensuring economic stability and business sustainability. The intelligent early warning model enhances decision-making by detecting potential risks before they escalate into significant financial losses. However, traditional models often rely on static indicators and linear assumptions, lacking the capacity to capture complex, temporal patterns in enterprise financial data. This limitation reduces their effectiveness in dynamic business environments. To address these challenges, this paper proposes a Bidirectional Long Short-Term Memory-Based Financial Risk Prediction (BiLSTM-FRP) model with an Attention Mechanism. The model leverages bidirectional long short-term memory networks to understand both forward and backward financial trends, while the attention layer prioritizes the most influential features that affect enterprise risk. This hybrid deep learning approach enables the system to generate real-time risk scores and trigger early warnings, allowing stakeholders to implement proactive strategies. Experimental results demonstrate that the proposed method significantly outperforms existing baseline models in accuracy, recall, and early detection performance. This confirms its potential as a reliable tool for financial institutions and enterprises in mitigating risk and enhancing financial resilience. The BiLSTM-FRP model outperformed four financial risk prediction variables, achieving high accuracy (0.93), strong recall (0.91), and effective early detection (0.90). Its financial resilience score (0.88) indicates robustness in volatile markets, highlighting its reliability and potential as a real-time financial risk management and prediction tool.