Data-Driven Financial Risk Mitigation in Energy Investments: Optimizing Capital Allocation and Portfolio Performance

Abstract

Aim: This study examines the extent to which data-driven financial risk mitigation practices assist in optimizing the usage of capital and portfolio performance in energy investments, particularly in the face of market volatility, regulatory risks, and geopolitical risks. Study Design: A review of literature on financial risk management techniques using big data, machine learning, and AI-based analytics to optimize investment-making in the energy sector. The study focuses on literature between 2019 to 2024. Methodology: This research utilizes a systematic literature peer review approach, analyzing studies in reputable databases such as Google Scholar, Scopus, SSRN, and Journal of Risk and Financial Management. Selected articles focus on financial risk assessment models, predictive analytics, and AI-driven investment optimization in the energy sector. Results: This review identifies 12 significant studies highlighting the application of AI-driven credit risk modeling, machine learning-based predictive analytics, and portfolio optimization through automation in energy financing. The findings indicate that data analytics maximize investment accuracy, reduce capital exposure, and maximize portfolio diversification in various energy sub-sectors, including renewable and conventional energy resources. These have practical implications for financial institutions, policymakers, and investors by improving risk assessment frameworks, informing regulatory compliance strategies, and enhancing decision-making in energy financing. Conclusions: Financial risk mitigation strategies, techniques that are data-driven are crucial to ensure maximum financial robustness of energy investments. Analytics with AI improve predictive power, ensuring maximum allocation of capital and reducing financial exposure. Scalability and flexibility across diverse regulatory environments of these technologies need to be investigated in future studies.