New Paradigm in Financial Technology Using Machine Learning Techniques and Their Applications

Due to the inherent risks and challenges associated with financial management, researchers have faced a significant obstacle when analyzing financial data. The necessity for developing innovative models to comprehend financial assets has become imperative due to the transformation of the foundational principles underpinning financial markets. In order to provide a precise representation of data, scholars have introduced various machine learning systems that have shown promising outcomes. Within the pages of this book chapter, we delve into the progression of machine learning in the realm of finance over the past decade, with a particular focus on its applications encompassing Algorithmic Trading, Fraud Detection and Prevention, Portfolio Management, and Loan Underwriting. Algorithmic Trading is a methodology that leverages machine learning algorithms to extract knowledge from data, enabling and enhancing essential investment endeavors. These algorithms can acquire rules or structures from data in pursuit of objectives such as reducing prediction errors. In an era where fraudsters continuously evolve and sharpen their tactics, maintaining constant vigilance is crucial to thwart fraud and stay one step ahead of malicious actors. It is imperative to be attuned to significant trends that can distinguish between legitimate and fraudulent transactions. This section comprehensively analyzes multiple machine learning algorithms, supported by examples. Moreover, the chapter delves into the examination of the impact of machine learning approaches in assessing credit risk and finance. It scrutinizes the limitations of recent studies and explores emerging research trends in this domain.