5W1H Framework for Application of AI in Power Transmission Companies for Financial Management

By leveraging AI adaptability for financial management in the power transmission organization a robust and self-sustaining financial system can be designed while having smooth operations. The paper has been designed using the 5W1H framework. The AI-based FMS supports preparing budgets, improving risk management, planning taxation, and drafting the legal financing documents that are driven by the maintenance plan, development portfolio and financial circumstances. Apart from the work support, AI is benefiting from automation despite the fact maintains accuracy and efficiency. The decision-making becomes speedy since we can have readily available reports and recommendations on few clicks away. However, the implementation of AI for FMS will also pose some challenges, which mainly indicate the data availability, IT infrastructure and dedicated financial and human resources. AI for FMS implementation generally falls within the purview of the finance department and is jointly carried out with the IT department. The application has been divided into different stages, starting from the avoidance to experimental reached to transformation by flowing through steadiness and expansion, respectively. So, this whole process covers the 5Ws, whereas, the 1H answers the question of how this path can be followed. Thus, need to develop the methodology for data gathering, machine learning, defining parameters and creating financial analysis and forecasting models. The successful execution can make it an indispensable decision-making tool for the organization.

Authors

Publication Details

Journal
Journal of Information Systems Engineering & Management
Published
2025-03-07
DOI
https://doi.org/10.52783/jisem.v10i17s.2713
Primary Topic
Energy Load and Power Forecasting
Type
article
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0.00
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article

5W1H Framework for Application of AI in Power Transmission Companies for Financial Management

Stephen Larkin
Journal of Information Systems Engineering & Management
Energy Load and Power Forecasting
article

5W1H Framework for Application of AI in Power Transmission Companies for Financial Management

Stephen Larkin
article en

Abstract

By leveraging AI adaptability for financial management in the power transmission organization a robust and self-sustaining financial system can be designed while having smooth operations. The paper has been designed using the 5W1H framework. The AI-based FMS supports preparing budgets, improving risk management, planning taxation, and drafting the legal financing documents that are driven by the maintenance plan, development portfolio and financial circumstances. Apart from the work support, AI is benefiting from automation despite the fact maintains accuracy and efficiency. The decision-making becomes speedy since we can have readily available reports and recommendations on few clicks away. However, the implementation of AI for FMS will also pose some challenges, which mainly indicate the data availability, IT infrastructure and dedicated financial and human resources. AI for FMS implementation generally falls within the purview of the finance department and is jointly carried out with the IT department. The application has been divided into different stages, starting from the avoidance to experimental reached to transformation by flowing through steadiness and expansion, respectively. So, this whole process covers the 5Ws, whereas, the 1H answers the question of how this path can be followed. Thus, need to develop the methodology for data gathering, machine learning, defining parameters and creating financial analysis and forecasting models. The successful execution can make it an indispensable decision-making tool for the organization.

Journal of Information Systems Engineering & ManagementVol. 10(17s)
Openalex Percentile: Top 97%
Energy Load and Power Forecasting
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5W1H Framework for Application of AI in Power Transmission Companies for Financial Management — Stephen Larkin · Journal of Information Systems Engineering & Management (2025) | TGRS Research Map | TGRS