A Deep Learning-Based Framework for Accurate Bankruptcy Prediction

Bankruptcy prediction is a critical challenge in financial risk management, as early identification of distressed firms can prevent severe economic consequences and aid stakeholders in making informed decisions. Traditional statistical models and traditional machine learning approaches have demonstrated limited accuracy in capturing the complex, non-linear relationships within financial datasets. To address these limitations, this work proposed a deep learning-based framework for accurate bankruptcy prediction. The proposed model leverages advanced neural network architectures to analyze multidimensional financial indicators, incorporating techniques such as feature normalization, dropout regularization, and adaptive optimization for improved performance and generalization. Experiments were conducted using publicly available bankruptcy datasets containing diverse financial ratios and company attributes. The model’s effectiveness was evaluated using standard metrics including Accuracy, Precision, Recall, and AUC-ROC. Experiment results indicate that the proposed deep learning approach significantly outperforms traditional methods, achieving superior predictive accuracy and robustness. This work highlights the potential of deep learning as an intelligent solution for proactive bankruptcy risk assessment, contributing to improved financial decision-making and risk mitigation strategies.