Machine Learning in Finance: Trends, Developments and Business Practices in the Financial Sector

This book discusses the evolution of technical features in decentralized finance and focuses on machine-learning finance in emerging economies. As technological advancement evolves at an unpredictable pace, the financial industry, like every other sector, must adapt accordingly. Furthermore, the rapid expansion of diverse financial products and services is creating new applications and markets. Alongside technological progress, the exploration of complex patterns in vast amounts of data, known as big data, is facilitated by its commonly acknowledged characteristics: volume, variety, veracity, value, and velocity.

Overall, machine learning has become crucial in the financial industry, allowing businesses to automate operations, gain insights from data, and make more informed decisions in real time. This edited book covers algorithmic trading, risk management, fraud detection, customer service and personalization, portfolio management, credit scoring, sentiment analysis, and algorithmic pricing. The book connects theoretical concepts with practical real-world applications, benefiting professionals looking to enhance their proficiency in using these methods efficiently. It offers insightful guidance for theorists, market participants, and policymakers by exploring financial theories and practices in light of contemporary machine-learning approaches, with a special emphasis on emerging economies.

Musa Gün is Associate Professor for Finance at the Recep Tayyip Erdoğan University in Rize (Türkiye). His research focuses on asset pricing models, market anomalies, credit risk management, investment valuations, and financial technologies.

Burcu Kartal is Assistant Professor in the Department of Quantitative Methods at the Recep Tayyip Erdoğan University in Rize (Türkiye). Her research interests include data mining, machine learning, text mining, qualitative methods, and metaheuristics.