A Deep Learning-Based Framework for Accurate Bankruptcy Prediction

Ajay Sourashtriya; Anurag Shrivastava

doi:10.71097/IJSAT.v16.i3.7194

A Deep Learning-Based Framework for Accurate Bankruptcy Prediction

Author(s)	Ajay Sourashtriya, Anurag Shrivastava
Country	India
Abstract	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.
Keywords	Bankruptcy, Bankruptcy prediction system Classification, machine learning techniques, Deep Learning, Credit Card Fraud Detection
Field	Engineering
Published In	Volume 16, Issue 3, July-September 2025
Published On	2025-07-29
DOI	https://doi.org/10.71097/IJSAT.v16.i3.7194
Short DOI	https://doi.org/g9vzf6

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A Deep Learning-Based Framework for Accurate Bankruptcy Prediction

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