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Developing an Advanced Predictive Model for Financial Planning and Analysis Using Machine Learning
Subject area: Science,Engineering and Technology · Area of research: Machine Learning
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
How to cite this paper
@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},
}