Artificial Intelligence in Financial Services

Concepts and the Applications
Erscheinungsjahr 2026
ISBN: 978-981-9232-14-7
Verlag: Springer

Buch, Englisch, 469 Seiten, Format (B × H): 155 mm x 235 mm

Artificial Intelligence in Financial Services is to show how machine learning is reshaping banking, insurance, and capital markets. Moving beyond buzzwords, the book shows professionals, students, and researchers AI matters in finance services—and to deploy it responsibly.

Organized in five concise parts, it opens with the fundamentals of machine learning, deep learning, and large language models. Then, the book walks readers through real-world use cases: credit-scoring engines that out-perform traditional logistic models, robo-advisors that rebalance portfolios in minutes, fraud-detection networks that save insurers millions, and high-frequency trading systems that mine news and social-media sentiment in real time. Dozens of corporate case studies, academic findings, and code-ready project ideas illustrate what works—and what fails—at each stage of the AI pipeline. Dedicated chapters on governance, regulation, explainable AI, and bias mitigation give readers the tools to satisfy regulators while protecting consumers. The final section forecasts how reinforcement learning, DeFi, and human–AI collaboration will shape the next decade of financial innovation.

Readers will learn to evaluate algorithms, engineer features for noisy time-series data, benchmark model performance, and anticipate ethical pitfalls. Finance professionals gain an action plan for piloting AI safely; students and researchers discover up-to-date research agendas and hands-on projects. A basic grasp of statistics and financial terminology is helpful, but no prior coding expertise is required.

Packed with practical insights and future-ready strategies, this book equips readers to turn artificial intelligence from headline hype into competitive advantage.

Zielgruppe

Graduate

Autoren/Hrsg.

Heo, Wookjae

Kim, Eunchan

Noh, Junhyug

Fachgebiete

• Wirtschaftswissenschaften Betriebswirtschaft Wirtschaftsmathematik und -statistik
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Wirtschaftswissenschaften Finanzsektor & Finanzdienstleistungen Finanzsektor & Finanzdienstleistungen: Allgemeines
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken

Inhaltsverzeichnis

Part I AI Theory and Machine-Learning Fundamentals.- Chapter 1: The History and Concept of AI.- Chapter 2: Machine-Learning Fundamentals.- Chapter 3: Deep Learning and Advanced AI Techniques.- Chapter 4: Data Pre-Processing and Feature Engineering.- Part II Applications in Financial Services.- Chapter 5: AI in Finance Overview.- Chapter 6: Credit Scoring and Loan Underwriting.- Chapter 7: Wealth Management and Robo-Advisors services.- Chapter 8: Risk Management and InsurTech Innovations.- Chapter 9: Algorithmic Trading and Market Analytics.- Chapter 10: Customer Engagement, Chatbots, and NLP Applications.- Part III Case Studies, Research, and Projects.- Chapter 11: Corporate and Institutional Case Studies.- Chapter 12: Academic Research Trends and Applications.- Chapter 13: Success and Failure Factors.- Part IV Ethics, Regulation, and Governance.- Chapter 14: Governance and Regulatory Issues.- Chapter 15: Explainable AI and Fairness.- Part V Future Outlook and Conclusions.- Chapter 16: The Future of Finance in an AI World.- Chapter 17: Conclusions and Recommendations.

Über Autor(innen)

Dr. Wookjae Heo, Associate Professor of Financial Counseling and Planning at Purdue University’s Division of Consumer Science, has made significant contributions to the field of financial well-being through innovative research and interdisciplinary scholarship. His academic journey began with a Ph.D. from the University of Georgia, where his dissertation leveraged data mining to explore the demand for life insurance, setting the stage for his future work that combines advanced analytics with financial studies. At the core of Dr. Heo’s research is a commitment to understanding and mitigating financial stress through the lens of psychological and behavioral economics. His pioneering integration of machine learning and data science into financial research has notably advanced the field's methodological landscape. He has earned multiple research awards from financial services academic associations—including the Academy of Financial Services and the Financial Therapy Association—where he has also served as a board director. In addition, he served multiple peer-reviewed journals associated with financial services as an editorial board member including and . 

Eunchan Kim is an Assistant Professor in the Department of Information Systems at Hanyang University, South Korea. He earned his Ph.D. in Engineering at Seoul National University. Before joining Hanyang University, he accumulated practical and interdisciplinary experience as a Senior Researcher and Manager at Hanwha Group, a Lecturer at Seoul National University, a Researcher at Korea Credit Bureau, and a Visiting Scholar at Seoul National University Hospital and Jeonbuk National University Hospital. His research interests include information systems, AI applications, data analytics, and engineering management. He also serves as the Director of the Data and Business Intelligence Lab. at Hanyang University, where his team conducts research on AI applications for solving heterogeneous business problems, algorithm development for different types of datasets, and policy-related studies on data usage and technology adoption.

Dr. Junhyug Noh is an Assistant Professor in the Department of Artificial Intelligence at Ewha Womans University, Seoul, where he directs the Practical AI (PAI) Lab. His research spans computer vision, weakly- and semi-supervised learning, and cross-domain AI applications (AI?+?X), with projects ranging from medical diagnostics and materials discovery to surveillance and process automation. Prior to joining Ewha, he was a postdoctoral researcher in the Computational Engineering Division at Lawrence Livermore National Laboratory (USA). Dr. Noh received his Ph.D. in Computer Science and Engineering from Seoul National University. He has published more than 25 peer-reviewed papers in leading venues such as CVPR, ICCV, ECCV, and Scientific Reports, and has won top-track awards in the Learning from Imperfect Data Challenge. An active member of the research community, he serves on program committees for CVPR, ECCV, ICCV, NeurIPS, and related journals, and is the recipient of the Naver Ph.D. Fellowship and an LLNL Division Recognition Award. At Ewha, Dr. Noh teaches machine learning, computer vision, and AI mathematics, mentoring students and practitioners on deploying robust, socially responsible AI systems.