Title: An Advanced Machine Learning Framework for Predictive Business Analytics in Corporate Finance
Authors: Emmanuel Damilare Balogun, Kolade Olusola Ogunsola, Adebanji Samuel Ogunmokun
Volume: 9
Issue: 3
Pages: 115-123
Publication Date: 2025/03/28
Abstract:
This paper explores the application of advanced machine learning (ML) frameworks in predictive business analytics within corporate finance. With the growing complexity of financial markets, traditional forecasting methods are increasingly being supplemented, if not replaced, by machine learning models that leverage vast amounts of financial data to improve decision-making processes. The study investigates the role of ML in enhancing financial forecasting, optimizing investment strategies, managing risks, and improving overall operational efficiency in the corporate finance sector. Through an in-depth analysis of existing literature, case studies, and machine learning techniques such as linear regression, random forests, and XGBoost, the paper highlights the significant advantages of these models in predicting key financial indicators, including stock prices, interest rates, and market movements. The findings suggest that machine learning techniques outperform traditional methods by providing real-time, data-driven insights that are adaptive to changing market conditions. However, challenges such as data quality, model interpretability, and regulatory concerns are also identified, with recommendations for addressing these hurdles to enhance the effectiveness of ML in corporate finance. The paper concludes with actionable recommendations for finance professionals, investors, and institutions to integrate ML frameworks into their business analytics processes, along with suggestions for future research directions, including the potential integration of deep learning, natural language processing, and big data with machine learning to optimize financial decision-making further.