Artificial intelligence and machine learning in finance: Addressing complex problems and ESG applications

The advancement of artificial intelligence (AI) and machine learning (ML) has substantially influenced multiple sectors, with the finance sector undergoing a particularly notable transformation. AI refers to computational systems designed to perform tasks typically associated with human cognition, including decision-making, perception, language comprehension, and reasoning (Vernon & Furlong, 2007; Lieto et al., 2018; Korteling et al., 2021).
The structure of this book unfolds across four chapters. Chapter 1 provides the technical foundation of financial AI systems, laying the groundwork for understanding the complex methodologies involved. Chapter 2 delves into predictive modeling and risk-oriented applications, exploring how AI and ML are used to address real-world challenges in finance. In Chapter 3, the focus shifts to AI’s role in financial strategies, including market behavior and the integration of ESG principles. Finally, Chapter 4 tackles open challenges and emerging trends in the field, including explainable AI and new paradigms in ethical ML. The conclusion synthesizes key insights and offers strategic recommendations for investors, policymakers, and researchers.
Through this exploration, this book aims to shed light on the transformative potential of AI and ML in finance, providing readers with the knowledge and tools to navigate the future of this rapidly evolving field. By equipping practitioners and scholars with a deeper understanding of both the power and the limitations of these technologies, the book also aims to encourage the development of AI-driven financial ecosystems that are not only efficient and profitable but also ethical, resilient, and aligned with broader societal goals.