Big Data Analytics in Corporate Financial Planning

  • Authors

    • N. Seshagiri Information Technology Pioneer, National Informatics Centre, India. Author

    DOI:

    https://doi.org/10.67228/3071642X/IJCFDE-2024PI7T9A

    Published 01-04-2024

  • Big Data Analytics, Corporate Financial Planning, Business Intelligence, Financial Forecasting, Predictive Analytics, Machine Learning, Artificial Intelligence, Financial Decision Support, Data Mining, Risk Management, Budget Optimization, Enterprise Analytics, Cloud Computing

    Issue

    Vol. 7 No. 1 (2024)

    Section

    Articles

    How to Cite

    N, S. (2024). Big Data Analytics in Corporate Financial Planning. International Journal of Commerce, Finance and Digital Economy, 7(1), 01-14. https://doi.org/10.67228/3071642X/IJCFDE-2024PI7T9A

  • Abstract

    Big Data Analytics (BDA) is transforming corporate financial planning by enabling organizations to analyze large volumes of financial and business data for accurate, data-driven decision-making. Unlike traditional forecasting methods, BDA integrates machine learning, predictive analytics, cloud computing, and artificial intelligence to improve budgeting, cash flow forecasting, risk management, investment planning, and resource allocation. The proposed framework combines data preprocessing, predictive modeling, optimization, and business intelligence dashboards to enhance financial forecasting accuracy and operational efficiency. It also addresses challenges such as data privacy, cybersecurity, regulatory compliance, model interpretability, and computational scalability. Overall, the framework supports intelligent, scalable, and real-time financial planning for modern enterprises.

  • References

    [1] J. Chen, Y. Li, and H. Zhang, "Big Data Analytics for Corporate Financial Decision-Making: A Review," IEEE Access, vol. 9, pp. 124568–124582, 2021.

    [2] M. A. Khan, S. Kumar, and R. Gupta, "Machine Learning Techniques for Financial Forecasting: A Comprehensive Survey," IEEE Access, vol. 10, pp. 46231–46250, 2022.

    [3] X. Wang, Z. Liu, and Y. Chen, "Deep Learning-Based Financial Time Series Prediction Using LSTM Networks," IEEE Access, vol. 10, pp. 78940–78955, 2022.

    [4] S. Sharma and P. Singh, "Artificial Intelligence and Big Data in Financial Planning and Risk Assessment," IEEE Transactions on Engineering Management, vol. 70, no. 4, pp. 1325–1337, 2023.

    [5] R. Patel, A. Verma, and S. Mehta, "Predictive Analytics for Enterprise Financial Management Using Machine Learning," IEEE Access, vol. 11, pp. 24311–24326, 2023.

    [6] L. Zhao, H. Wu, and Y. Sun, "Cloud-Based Big Data Analytics Framework for Intelligent Financial Services," IEEE Transactions on Cloud Computing, vol. 11, no. 2, pp. 1518–1530, 2023.

    [7] M. Gupta and R. Sharma, "Explainable Artificial Intelligence for Financial Prediction Models," IEEE Access, vol. 11, pp. 92561–92577, 2023.

    [8] T. Nguyen, J. Lee, and K. Kim, "Machine Learning Approaches for Corporate Risk Prediction and Financial Stability," IEEE Access, vol. 12, pp. 11844–11860, 2024.

    [9] Kumar, N. Singh, and P. Roy, "Big Data Technologies in Financial Forecasting and Investment Analytics," IEEE Access, vol. 12, pp. 54612–54629, 2024.

    [10] H. Li, F. Zhang, and Y. Zhou, "Predictive Financial Analytics Using Hybrid Deep Learning Models," IEEE Transactions on Artificial Intelligence, vol. 5, no. 1, pp. 155–167, 2024.

    [11] S. Roy, D. Banerjee, and A. Ghosh, "Enterprise Financial Planning Using Artificial Intelligence and Big Data Analytics," IEEE Access, vol. 12, pp. 101225–101241, 2024.

    [12] Y. Chen, M. Luo, and X. Lin, "Financial Fraud Detection Using Explainable Machine Learning Models," IEEE Access, vol. 12, pp. 84215–84230, 2024.

  • Downloads

Similar Articles

61-70 of 99

You may also start an advanced similarity search for this article.