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As the global economy grows in complexity, corporate financial risk management becomes increasingly vital. It is directly related to the economic interests of the company and can also affect the stability of the broader financial market. However, the prediction of corporate financial risks is fraught with many challenges: the nonlinear relationship and volatility of financial data make it tough for traditional forecasting methods to capture potential risk signals; the rapid changes in the economic environment and policies limit the reliability of historical data (Wang, 2022; Landi, 2022). Early warning of financial risks is crucial for companies. It enables timely measures to reduce potential losses and provide valuable information for stakeholders like investors and banks. With the advent of big data and AI technologies, deep learning and machine learning models are increasingly applied in this field (Guan, 2021; Li, 2023; Zhao, 2023). In the field of enterprise financial risk management and early warning, there are the following three representative traditional models:
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Linear Regression: Linear regression is a method that describes the relationship between independent variables and dependent variables through a linear equation. This method is simple, easy to understand, and interpret. However, it may not be accurate enough for nonlinear data and may suffer from underfitting (Petrella and Raponi, 2019; Izzah, 2017).
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Support Vector Machines (SVM): SVM is a classification method that finds a hyperplane to maximize the margin between two classes. SVM performs well in high-dimensional spaces and can handle nonlinear problems. However, training time can belong for large datasets, and appropriate kernel functions need to be selected(Kurani, 2023; Huang, 2020).
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Long Short-Term Memory (LSTM): LSTM is a special type of recurrent neural network that can learn long-term dependencies and is suitable for handling time series data. It can capture long-term relationships effectively. However, LSTM also requires a large amount of data and computational resources, and training time may be longer(Kakade, 2022; Rodikov, 2022).
The issue of early warning of enterprise financial risks is complex. Traditional methods are often based on statistical and economic theories. However, deep learning and machine learning offer new solutions. Neural network models have the capability to process large amounts of financial data, capture complex nonlinear relationships, and enhance the accuracy of early warning systems, which presents new opportunities in the field. It is crucial to recognize that different models come with distinct characteristics and limitations. Therefore, it is crucial to select and optimize the appropriate in order for efficient early warning.
This article presents an innovative approach that combines three deeplearning techniques BPNN, Bi-LSTM, and transfer learning,to enhance the accuracy of financial risk prediction and warning. The integrated system aims to capture nonlinear relationships, temporal dependencies, and leverage pre-trained models' knowledge to improve risk prediction accuracy. Compared to traditional methods, the approach in this article offers several advantages and innovative features: nonlinear modeling, feature extraction, and transfer learning. Firstly, by combining BPNN and Bi-LSTM, the system captures complex nonlinear relationships and temporal dependencies, surpassing the limitations of linear models. Secondly, the integrated system effectively extracts informative features from financial data, utilizing the strengths of BPNN and Bi-LSTM. This enhances the system's representation capabilities and improves risk prediction accuracy. Thirdly, incorporating transfer learning allows the system to benefit from pre-trained models, enhancing its generalization abilities and reducing the dependence on large-scale labeled data.