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Abstract

Purpose: This study explores the impact of integrating machine learning algorithms and big data analytics on risk assessment and management, focusing on financial, strategic, environmental, social, and governance (ESG) perspectives.


Research Design and Methodology: The research utilizes a comprehensive literature review to analyze the benefits, challenges, and implications of incorporating machine learning and big data analytics into risk management frameworks. It synthesizes insights from scholarly articles, empirical studies, and regulatory documents to provide a holistic understanding.


Findings and Discussion: The findings reveal that integrating machine learning and big data analytics significantly enhances risk measurement and management in strategic financing decisions. These technologies improve risk assessment accuracy, help identify emerging risks, and enable organizations to capitalize on market opportunities. Including ESG criteria in risk management frameworks further strengthens organizational resilience by addressing non-financial risks.


Implications: The study underscores the need for innovative risk management practices to navigate uncertainties and seize opportunities in a complex, interconnected environment. It highlights the importance of leveraging technological advancements and incorporating ESG considerations into risk management to enhance organizational resilience, drive long-term value creation, and support sustainable development. Future research should explore further innovations in risk management frameworks.

Keywords

Machine Learning Big Data Analytics Risk Management Environmental Social Governance ESG

Article Details

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite
Hadijah, A. S., & Karmila, K. (2024). Evaluating the Role, Costs, and Benefits of Insurance and Hedging in Financing Decisions. Advances in Economics & Financial Studies, 2(2), 88–101. https://doi.org/10.60079/aefs.v2i2.313
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