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@article{206557,
        author = {Dr. Divya Chitre and Dr. Tejashri Phalle and Mrs. Shubhangi Chavan},
        title = {An Explainable Machine Learning Framework for ESG-Driven Financial Risk Prediction Using Multi-Source Sustainability Indicators},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {2},
        pages = {2199-2209},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=206557},
        abstract = {Financial institutions and investors increasingly recognize that long-term financial stability depends not only on conventional financial indicators but also on environmental, social, and governance (ESG) performance. The growing emphasis on sustainable finance has encouraged organizations to integrate ESG information into financial risk assessment and investment decision-making. However, existing risk prediction models often rely on structured financial variables while overlooking valuable sustainability-related information available from diverse sources such as corporate sustainability reports, ESG rating agencies, regulatory disclosures, news articles, and stakeholder sentiment. Furthermore, many advanced machine learning models provide high predictive accuracy but suffer from limited transparency, making their adoption challenging in highly regulated financial environments where explainability and accountability are essential. This paper proposes an explainable machine learning framework that integrates multi-source sustainability indicators with traditional financial variables to improve financial risk prediction while maintaining model interpretability. The proposed framework combines structured financial data, ESG performance indicators, sustainability disclosures, and external textual information to develop a comprehensive representation of organizational risk. Advanced machine learning algorithms are employed for predictive modeling, whereas Explainable Artificial Intelligence (XAI) techniques, including SHAP (SHapley Additive exPlanations) and Local Interpretable Model-agnostic Explanations (LIME), are incorporated to provide transparent explanations for model predictions. The proposed framework aims to support investors, financial institutions, policymakers, and corporate decision-makers by providing reliable, interpretable, and sustainable financial risk assessments. Unlike conventional approaches that primarily emphasize predictive performance, this research balances prediction accuracy with model transparency and sustainability considerations. The framework also contributes to responsible artificial intelligence by promoting trustworthy decision support systems aligned with sustainable finance objectives. The proposed study establishes a foundation for future empirical validation using publicly available ESG and financial datasets and offers a practical roadmap for integrating explainable machine learning into ESG-based financial risk management.},
        keywords = {Explainable Artificial Intelligence, Machine Learning, ESG, Financial Risk Prediction, Sustainable Finance, SHAP, LIME, Sustainability Indicators, Responsible AI, Decision Support Systems.},
        month = {July},
        }