Background: The energy industry requires enormous financial investment
across various sub-sectors, including oil, natural gas, nuclear energy, and
renewable energy technologies. Financial risk management remains a critical
concern within the sector. With the development in artificial intelligence
(AI), machine learning (ML), and big data analytics, financial risk management
processes have been greatly transformed by furnishing real-time information,
predictive analysis, and automated decision-making processes.
Aim: This study examines the extent to which data-driven financial
risk mitigation practices assist in optimising the usage of capital and
portfolio performance in energy investments, particularly in the face of market
volatility, regulatory risks, and geopolitical risks.
Methodology: This study employs a systematic literature review to
analyse 12 empirical studies published between 2019 and 2024. This chapter
reviews recent studies that use AI tools such as machine-learning models,
predictive analytics, and automated portfolio methods. The review was conducted
using reputable databases such as Google Scholar, Scopus, SSRN, and the Journal
of Risk and Financial Management. Selected articles focus on financial risk
assessment models, predictive analytics, and AI-driven investment optimisation
in the energy sector.
Results: This review highlighted the application of AI-driven
credit risk modelling, machine learning-based predictive analytics, and
portfolio optimisation through automation in energy financing. These advanced
analytical tools have empowered investors to effectively deal with market
volatility, regulatory risks, and geopolitical threats. The findings also
indicate that data analytics maximise investment accuracy, reduce capital
exposure, and optimise 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 optimal allocation
of capital and reducing financial exposure. However, the scalability of AI
models in numerous regulatory environments is a major issue because various
data governance rules and compliance levels might limit their use. Scalability
and flexibility across diverse regulatory environments of these technologies
need to be investigated in future studies.
Author
(s) Details
Ebere Juliet Onyeka
The George Washington University, United States.
Please see the book here:- https://doi.org/10.9734/bpi/nabme/v8/5643
