Developing an Advanced Predictive Model for Financial Planning and Analysis Using Machine Learning

References

[1] Abisoye, A., & Akerele, J. I. (2022a). A Practical Framework for Advancing Cybersecurity, Artificial Intelligence and Technological Ecosystems to Support Regional Economic Development and Innovation.

[2] Abisoye, A., & Akerele, J. I. (2022b). A Scalable and Impactful Model for Harnessing Artificial Intelligence and Cybersecurity to Revolutionize Workforce Development and Empower Marginalized Youth.

[3] Achumie, G. O., Oyegbade, I. K., Igwe, A. N., Ofodile, O. C., & Azubuike, C. (2022). AI-driven predictive analytics model for strategic business development and market growth in competitive industries. J Bus Innov Technol Res.

[4] Adaralegbe, A. A., Egbuchiem, H., Adeoti, O., Abbasi, K., Ezeani, E., Adaralegbe, N. J.-F., . . . Ayeni, O. (2022). Do personality traits influence the association between depression and dementia in old age? Gerontology and Geriatric Medicine, 8, 23337214211068257.

[5] Adewoyin, M. A. (2021). Developing frameworks for managing low-carbon energy transitions: overcoming barriers to implementation in the oil and gas industry.

[6] Adewoyin, M. A. (2022). Advances in risk-based inspection technologies: Mitigating asset integrity challenges in aging oil and gas infrastructure.

[7] Ajayi, A., & Akerele, J. I. (2021). A High-Impact Data-Driven Decision-Making Model for Integrating Cutting-Edge Cybersecurity Strategies into Public Policy, Governance, and Organizational Frameworks. International Journal of Multidisciplinary Research and Growth Evaluation, 2(1), 623-637. doi:https://doi.org/10.54660/IJMRGE.2021.2.1.623-637.

[8] Ajayi, A., & Akerele, J. I. (2022a). A Practical Framework for Advancing Cybersecurity, Artificial Intelligence and Technological Ecosystems to Support Regional Economic Development and Innovation. International Journal of Multidisciplinary Research and Growth Evaluation, 3(1), 700-713. doi:https://doi.org/10.54660/IJMRGE.2022.3.1.700-713.

[9] Ajayi, A., & Akerele, J. I. (2022b). A Scalable and Impactful Model for Harnessing Artificial Intelligence and Cybersecurity to Revolutionize Workforce Development and Empower Marginalized Youth. International Journal of Multidisciplinary Research and Growth Evaluation, 3(1), 714-719. doi:https://doi.org/10.54660/IJMRGE.2022.3.1.714-719.

[10] Athey, S. (2018). The impact of machine learning on economics. In The economics of artificial intelligence: An agenda (pp. 507-547): University of Chicago Press.

[11] Chai, T., & Draxler, R. R. (2014). Root mean square error (RMSE) or mean absolute error (MAE). Geoscientific model development discussions, 7(1), 1525-1534.

[12] Chicco, D., Warrens, M. J., & Jurman, G. (2021). The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. Peerj computer science, 7, e623.

[13] Dutta, A., Bandopadhyay, G., & Sengupta, S. (2012). Prediction of stock performance in the Indian stock market using logistic regression. International Journal of Business and Information, 7(1), 105.

[14] Elumilade, O. O., Ogundeji, I. A., Achumie, G. O., Omokhoa, H. E., & Omowole, B. M. (2021). Enhancing fraud detection and forensic auditing through data-driven techniques for financial integrity and security. Journal of Advanced Education and Sciences, 1(2), 55-63.

[15] Elumilade, O. O., Ogundeji, I. A., Achumie, G. O., Omokhoa, H. E., & Omowole, B. M. (2022). Optimizing corporate tax strategies and transfer pricing policies to improve financial efficiency and compliance. Journal of Advance Multidisciplinary Research, 1(2), 28-38.

[16] Guidotti, R., Monreale, A., Ruggieri, S., Turini, F., Giannotti, F., & Pedreschi, D. (2018). A survey of methods for explaining black box models. ACM computing surveys (CSUR), 51(5), 1-42.

[17] Hassan, Y. G., Collins, A., Babatunde, G. O., Alabi, A. A., & Mustapha, S. D. (2021). AI-driven intrusion detection and threat modeling to prevent unauthorized access in smart manufacturing networks. Artificial intelligence (AI), 16.

[18] Hirschey, M., & Wichern, D. W. (1984). Accounting and market-value measures of profitability: Consistency, determinants, and uses. Journal of Business & Economic Statistics, 2(4), 375-383.

[19] Honegger, M. (2018). Shedding light on black box machine learning algorithms: Development of an axiomatic framework to assess the quality of methods that explain individual predictions. arXiv preprint arXiv:1808.05054.

[20] Ike, C. C., Ige, A. B., Oladosu, S., Adepoju, P., & Afolabi, A. I. (1769). Advancing Predictive Analytics Models for Supply Chain Optimization in Global Trade Systems. International Journal of Applied Research in Social Sciences. https://doi. org/10.51594/ijarss. v6i12.

[21] Kokina, J., Gilleran, R., Blanchette, S., & Stoddard, D. (2021). Accountant as digital innovator: Roles and competencies in the age of automation. Accounting Horizons, 35(1), 153-184.

[22] Konstantinov, A. V., & Utkin, L. V. (2021). Interpretable machine learning with an ensemble of gradient boosting machines. Knowledge-Based Systems, 222, 106993.

[23] Nabi, R. M., Soran Ab M, S., & Harron, H. (2020). A novel approach for stock price prediction using gradient boosting machine with feature engineering (gbm-wfe). Kurdistan Journal of Applied Research, 5(1), 28-48.

[24] Nayak, S., Misra, B. B., & Behera, H. S. (2014). Impact of data normalization on stock index forecasting. International Journal of Computer Information Systems and Industrial Management Applications, 6, 13-13.

[25] Odio, P. E., Kokogho, E., Olorunfemi, T. A., Nwaozomudoh, M. O., Adeniji, I. E., & Sobowale, A. (2021). Innovative financial solutions: A conceptual framework for expanding SME portfolios in Nigeria's banking sector. International Journal of Multidisciplinary Research and Growth Evaluation, 2(1), 495-507.

[26] Oladosu, S. A., Ige, A. B., Ike, C. C., Adepoju, P. A., Amoo, O. O., & Afolabi, A. I. (2022). Revolutionizing data center security: Conceptualizing a unified security framework for hybrid and multi-cloud data centers. Open Access Research Journal of Science and Technology, 5(2), 086-076.

[27] Onukwulu, E. C., Fiemotongha, J. E., Igwe, A. N., & Ewim, C. P.-M. (2022). International Journal of Management and Organizational Research.

[28] Otokiti, B. O. (2012). Mode of Entry of Multinational Corporation and their Performance in the Nigeria Market. Covenant University,

[29] Otokiti, B. O., Igwe, A. N., Ewim, C., Ibeh, A. I., & Sikhakhane-Nwokediegwu, Z. (2022). A framework for developing resilient business models for Nigerian SMEs in response to economic disruptions. Int J Multidiscip Res Growth Eval, 3(1), 647-659.

[30] Otokiti, B. O., Igwe, A. N., Ewim, C. P.-M., & Ibeh, A. I. (2021). Developing a framework for leveraging social media as a strategic tool for growth in Nigerian women entrepreneurs. Int J Multidiscip Res Growth Eval, 2(1), 597-607.

[31] Parmezan, A. R. S., Souza, V. M., & Batista, G. E. (2019). Evaluation of statistical and machine learning models for time series prediction: Identifying the state-of-the-art and the best conditions for the use of each model. Information sciences, 484, 302-337.

[32] Patel, J., Shah, S., Thakkar, P., & Kotecha, K. (2015). Predicting stock and stock price index movement using trend deterministic data preparation and machine learning techniques. Expert systems with applications, 42(1), 259-268.

[33] Paul, P. O., Abbey, A. B. N., Onukwulu, E. C., Agho, M. O., & Louis, N. (2021). Integrating procurement strategies for infectious disease control: Best practices from global programs. prevention, 7, 9.

[34] Podhorská, I., Vrbka, J., Lazaroiu, G., & Kovacova, M. (2020). Innovations in financial management: Recursive prediction model based on decision trees.

[35] Soini, K. (2020). Financial Planning & Analysis Tool for Commissioning Company X in Architectural Design.

[36] Steiner, G. A. (2010). Strategic planning: Simon and Schuster.

[37] Suykens, J. A., Vandewalle, J. P., & De Moor, B. L. (2012). Artificial neural networks for modelling and control of non-linear systems: Springer Science & Business Media.

[38] Tsai, C.-F., & Chiou, Y.-J. (2009). Earnings management prediction: A pilot study of combining neural networks and decision trees. Expert systems with applications, 36(3), 7183-7191.

[39] Vasilev, I., Slater, D., Spacagna, G., Roelants, P., & Zocca, V. (2019). Python Deep Learning: Exploring deep learning techniques and neural network architectures with Pytorch, Keras, and TensorFlow: Packt Publishing Ltd.

[40] Wasserbacher, H., & Spindler, M. (2022). Machine learning for financial forecasting, planning and analysis: recent developments and pitfalls. Digital Finance, 4(1), 63-88.

[41] Zhang, C., Patras, P., & Haddadi, H. (2019). Deep learning in mobile and wireless networking: A survey. IEEE communications surveys & tutorials, 21(3), 2224-2287.

[42] Zhang, Y., Xiong, F., Xie, Y., Fan, X., & Gu, H. (2020). The impact of artificial intelligence and blockchain on the accounting profession. Ieee Access, 8, 110461-110477.

[43] Zhou, J., Gandomi, A. H., Chen, F., & Holzinger, A. (2021). Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics, 10(5), 593.

@article{1703426,
      author = {Emmanuel Damilare Balogun, Kolade Olusola Ogunsola, Adebanji Samuel Ogunmokun},
      title = {Developing an Advanced Predictive Model for Financial Planning and Analysis Using Machine Learning},
      journal = {Iconic Research And Engineering Journals},
      year = {2022},
      volume = {5},
      number = {11},
      pages = {320-331},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1703426.pdf},
      abstract = {This paper explores the development and implementation of an advanced predictive model for financial planning and analysis (FP&A) using machine learning techniques. The traditional methods of financial forecasting, often reliant on historical data and static assumptions, present limitations in handling complex and dynamic financial environments. In contrast, machine learning models, particularly those utilizing decision trees, random forests, and gradient boosting, offer the ability to process vast amounts of data and generate more accurate and timely forecasts. This research demonstrates the advantages of machine learning in enhancing the accuracy of financial predictions, assisting in real-time decision-making, risk management, and long-term strategic planning. The model?s performance is assessed using key metrics such as mean squared error (MSE), root mean squared error (RMSE), and R-squared, showcasing significant improvements over traditional forecasting methods. However, challenges related to data quality, overfitting, and interpretability are acknowledged, with suggestions for addressing these limitations. The paper also highlights the potential for integrating machine learning models into FP&A processes to optimize financial decision-making and increase efficiency. Finally, it identifies key areas for future research, including improving model generalization, incorporating real-time data, and extending applications to other areas of finance.},
      keywords = {Financial Planning and Analysis (FP&A), Predictive Modeling, Machine Learning, Decision Trees, Financial Forecasting, Risk Management},
      month = {May},
  }