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@article{199499,
        author = {Safal Dnyaneshwar Madke and Prof.Abhijeet Gajbhiye},
        title = {Financial Performance Analysis Using Predictive Analytics: A Case Study of Maharashtra State Electricity Distribution Company Limited},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {13762-13770},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=199499},
        abstract = {In the emerging economies, especially in the field of power distribution, financial sustainability in the utilities in the public sector has been an issue of paramount concern. As the largest electricity distribution corporation in India, Maharashtra State Electricity Distribution Company Limited (MSEDCL) has a complex financial setup with high operational costs and restrictive regulations as well as socio-political requirements, including subsidized tariffs. This paper discusses the financial performance of MSEDCL by applying the conventional analysis of financial performance and the use of innovative predictive analytics tools. The studies use ratio analysis, time-series models, as well as machine learning models to assess historical trends and forecast subsequent financial performance. According to the study, the financial health of MSEDCL despite its notable revenue generation potential owing to its large consumer base is limited by its long-term problems including high aggregate technical and commercial losses, accumulated debt and slow pace in reimbursement of subsidies. Predictive analytics models are revealing that there is moderate improvement in financial stability, which depends on policy reforms, operational efficiency, and the application of technologies. The results highlight the importance of data-driven decision-making in the financial resilience and provide recommendations that predictive analytics should be used as a strategic instrument toward an increased efficiency, risk-resilience forecasting, and long-term sustainability in power distribution companies.},
        keywords = {Financial Performance, Predictive analytics, electricity distribution, utilities (public sector), time series forecasting, machine learning, financial ratios, energy economics, MSEDCL, risk analysis.},
        month = {April},
        }