Predictive Analytics in Fintech: Improving Investment Strategies with Machine Learning

Abstract

The integration of predictive analytics in financial technology (fintech) represents a transformative advancement in investment strategies, driven by the capabilities of machine learning (ML). This review paper explores the evolution and current state of predictive analytics within the fintech sector, emphasizing how ML techniques enhance investment decision-making. We systematically analyze key methodologies employed in predictive modeling, including supervised learning, unsupervised learning, and reinforcement learning, and their application to forecasting market trends, risk assessment, and portfolio optimization. By synthesizing recent advancements and case studies, we highlight how these technologies offer significant improvements in predictive accuracy and investment performance. The paper also addresses challenges related to data quality, model interpretability, and algorithmic biases, providing a comprehensive overview of strategies to mitigate these issues. Furthermore, we discuss the implications of emerging technologies, such as quantum computing and deep learning, on the future landscape of predictive analytics in fintech. Through a critical examination of current research and industry practices, this review aims to provide valuable insights into how predictive analytics can be effectively leveraged to enhance investment strategies, ultimately contributing to more informed and strategic financial decision-making. This paper serves as a foundational resource for both researchers and practitioners seeking to understand and implement advanced predictive models within the rapidly evolving fintech environment.