Machine learning in finance: transformation of financial markets

Abstract

This study explores the transformative role of machine learning in the financial sector, highlighting its evolution, methodologies, and diverse applications. Driven by the force of improvements in artificial intelligence, machine learning has fundamentally transformed traditional finance such that systems can now utilize big data, identify patterns, and make data-driven decisions with little to no human input. The study describes core types of machine learning: supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning with application examples in algorithmic trading, credit scoring, fraud detection, customer service, portfolio management, and insurance underwriting. Algorithmic trading, predictive analytics, and robo advisors turned possible by machine learning have made capital markets more efficient while delivering personalized financial services and security mechanisms with the help of machine learning. However, adopting machine learning raises ethical, regulatory, and data privacy issues. Focusing on the challenges and advancements, this paper offers a detailed examination of how machine learning reshapes financial markets while laying the groundwork for future work to make sense of its growing complexity.