Leveraging Predictive Analytics in Financing Decision-Making for Comparative Analysis and Optimization

Abstract

Purpose: This study explores the use of predictive analytics in financing decision-making, focusing on comparative analysis and optimization. The objective is to understand how predictive models enhance strategic planning and risk management in the financial sector. Research Design and Methodology: Employing a qualitative research approach, this study conducts a systematic literature review. Relevant scholarly articles, research papers, and reports from academic databases are analyzed to extract key findings and insights. Thematic analysis is utilized to identify recurring themes and trends. Findings and Discussion: The findings reveal that predictive analytics significantly improves credit risk assessment, investment management, customer segmentation, and fraud detection. By leveraging historical data and advanced algorithms, financial institutions can make more informed decisions, optimize asset allocation, and personalize customer interactions. However, challenges such as data quality, model interpretability, and regulatory compliance must be addressed to fully realize the benefits. Implications: The study highlights the need for robust data governance frameworks, ethical considerations, and interdisciplinary collaboration to ensure responsible use of predictive analytics in finance. Financial institutions are encouraged to invest in advanced analytics capabilities and foster a culture of data-driven decision-making. Future research should focus on emerging trends, real-world applications, and the development of ethical guidelines to support sustainable growth and innovation in the finance industry.