Enhancing Risk Prediction Framework for Corporate Financial Management Using Optimized Neural Network Strategies

Abstract

Risk prediction is crucial in corporate financial management, influencing strategic decision-making, investment optimization, and proactive mitigation. Traditional models struggle to handle modern financial data's complexity, nonlinearity, and temporal volatility. To address these limitations, this research proposes an advanced, intelligent risk prediction framework based on a Siberian Tiger Optimization-driven Temporal Convolutional Neural Network (STO-TCN). The framework is specifically designed to improve predictive accuracy, adaptability, and robustness in fluctuating financial environments. Research utilizes a comprehensive dataset comprising publicly available corporate financial statements, stock exchange disclosures, and macroeconomic indicators across diverse industry sectors. To enhance data integrity and model performance, two preprocessing techniques were applied: z-score standardization to ensure uniform data scaling and outlier detection to minimize the distortion caused by anomalous data entries. The TCN component effectively captures sequential patterns in financial time-series data, while the STO algorithm optimizes model hyperparameters and network weights, accelerating convergence and reducing overfitting. Experimental results demonstrate that the STO-TCN framework significantly outperforms traditional models with an accuracy of 0.9844, particularly in highly dynamic market scenarios using Python. This predictive framework offers a scalable and adaptive solution for corporate financial risk assessment, with practical applications in investment planning, regulatory compliance, financial governance, and enterprise sustainability. Further investigation incorporates real-time data streams and evaluates performance in small and medium-sized enterprises (SMEs) to broaden its applicability.