Predicting Financial Distress: ESG Scoring and Deep Learning Integration

In the fast-paced world of financial analysis, predicting and managing corporatefinancial distress has seen major changes, thanks to technological advancementsand a greater focus on sustainability. This review paper looks into howEnvironmental, Social, and Governance (ESG) scoring and deep learning canimprove the accuracy and scope of financial distress prediction models. ESGscoring evaluates a company's sustainability practices, providing indirectindicators of financial health and risk, thus highlighting the growing importanceof corporate responsibility for financial stability. Meanwhile, deep learning, abranch of artificial intelligence, uses advanced computational techniques toanalyze complex data sets and uncover patterns that traditional models mightmiss.The paper compares ESG scoring and deep learning, noting that while ESGscoring gives valuable insights into long-term sustainability risks, deep learningmodels provide immediate, data-driven predictions of financial health. It alsodiscusses how integrating these methods can offer a more comprehensive view ofa company's financial status by considering both sustainability factors andadvanced analytics. Emerging trends like alternative data sources and naturallanguage processing point to a future where financial distress prediction modelsare more dynamic, adaptable, and aligned with broader societal values.Despitethese advancements, challenges such as data quality, model interpretability, andethical considerations remain, necessitating ongoing research and development.The paper concludes by emphasizing the need for interdisciplinary collaboration,investment in data infrastructure, and continuous learning to fully harness thebenefits of ESG scoring and deep learning in predicting financial distress,ultimately contributing to a more sustainable and resilient financial system.

Keywords:

Financial Distress , Deep Learning , ESG Scoring