INTEGRATING DEEP LEARNING AND PERSISTENT HOMOLOGY FOR ENHANCED FINANCIAL RISK ASSESSMENT

Abstract

Accurately predicting bankruptcy is a critical challenge for financial institutions, businesses, and policymakers. Traditional bankruptcy prediction models rely on financial ratios and historical trends, but they often fail to capture complex patterns within financial data. With the increasing availability of large-scale financial datasets, there is a growing need for more advanced methodologies that can enhance predictive accuracy and provide deeper insights into financial risk assessment.The necessity for improved bankruptcy prediction stems from the economic and societal consequences of corporate financial distress. Ineffective risk assessment can lead to cascading failures in financial markets, supply chains, and employment sectors. As financial systems become more intricate, the ability to predict and mitigate bankruptcy risk is crucial for maintaining stability, reducing losses, and informing strategic decision-making. Deep learning techniques offer a promising avenue for capturing the temporal dependencies in financial data, while topological data analysis provides structural insights that go beyond conventional feature engineering. This dissertation presents a unique approach to bankruptcy prediction by integrating deep learning models with topological data analysis. While existing models rely primarily on statistical methods, this study incorporates Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) networks to analyze temporal financial trends. Additionally, persistent homology is leveraged to extract topological features, addressing gaps in traditional risk assessment models. By combining these methodologies, this research aims to improve prediction accuracy and enhance the interpretability of financial risk factors. To validate the effectiveness of the proposed approach, multiple evaluation techniques are employed. Model performance is assessed using standard machine learning metrics such as accuracy, precision, recall, and F1-score. Comparative analysis is conducted against traditional bankruptcy prediction models, highlighting the advantages of integrating deep learning and topological insights. Robustness tests are performed to ensure the reliability of extracted features, and explainability metrics are used to interpret the influence of topological data in bankruptcy forecasting. The results of this study demonstrate significant improvements in bankruptcy prediction accuracy compared to conventional models. By integrating deep learning and PH, this research provides a novel framework that enhances financial risk assessment. The findings have valuable implications for financial institutions seeking to refine predictive analytics, reduce investment risks, and improve early warning systems. Exploring PH in temporal data is crucial for advancing financial forecasting and risk assessment, especially when combined with deep learning models, enabling the development of more sophisticated, data-driven decision-making strategies.