Artificial Intelligence and Machine Learning-Powered Smart Finance

ISBN-13: 9798369332641 / Angielski / Twarda / 2024 / 347 str.

Sanjay Taneja, Amandeep Singh, Pawan Kumar

Opis

In the field of finance, the pervasive influence of algorithms has transformed the very fabric of the industry. Today, over 75% of trades are orchestrated by algorithms, making them the linchpin for trade automation, predictions, and decision-making. This algorithmic reliance, while propelling financial services into unprecedented efficiency, has also ushered in a host of challenges. As the financial sector becomes increasingly algorithm-driven, concerns about risk assessment, market manipulation, and the ethical implications of automated decision-making have taken center stage. Artificial Intelligence and Machine Learning-Powered Smart Finance, meticulously examines the intersection of computational finance and advanced algorithms and the challenges associated with this technology. As algorithms permeate various facets of financial services, the book takes a deep dive into their applications, spanning forecasting, portfolio optimization, market trends analysis, and cryptoanalysis. It sheds light on the role of AI-based algorithms in personnel selection, implementing trusted financial services, developing recommendation systems for financial platforms, and detecting fraud, presenting a compelling case for the integration of innovative solutions in the financial sector. As the book unravels the intricate tapestry of algorithmic applications in finance, it also illuminates the ethical considerations and governance frameworks essential for navigating the delicate balance between technological innovation and responsible financial practices. This book positions itself as a guide for academic scholars grappling with the multifaceted challenges posed by algorithm-driven FinTech. It not only dissects current industry practices but also provides a forward-looking perspective on the future of the financial industry. From algorithm-based financial solutions for stock markets, portfolio optimization, to fraud detection, each chapter offers a rich repository of research findings, case studies, best practices, and conceptual insights. Tailored to meet the intellectual needs of undergraduate, graduate, and executive students, as well as researchers in business, finance, economics, and technology, this book serves as a valuable resource for navigating the complexities of the contemporary financial landscape. It is equally relevant for practitioners, industry leaders, policymakers, investors, and corporate executives seeking to stay ahead of the curve in the ever-evolving world of algorithmic finance.