{"product_id":"9789819232147","title":"Artificial Intelligence in Financial Services Concepts and the Applications","description":"\u003ch1\u003eArtificial Intelligence in Financial Services\u003c\/h1\u003e\u003ch2\u003eConcepts and the Applications\u003c\/h2\u003e\u003ch3\u003eWookjae Heo | Eunchan Kim | Junhyug Noh\u003c\/h3\u003e\u003cdiv\u003e\u003cb\u003eMathematics \/ Probability \u0026amp; Statistics \/ General\u003c\/b\u003e\u003c\/div\u003e\u003cbr\u003e\u003cdiv\u003e\n\u003cp\u003e\u003cstrong\u003eArtificial Intelligence in Financial Services\u003c\/strong\u003e is to show how machine learning is reshaping banking, insurance, and capital markets. Moving beyond buzzwords, the book shows professionals, students, and researchers \u003cem\u003ewhy\u003c\/em\u003e AI matters in finance services—and \u003cem\u003ehow\u003c\/em\u003e to deploy it responsibly.\u003c\/p\u003e\n\u003cp\u003eOrganized 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.\u003c\/p\u003e\n\u003cp\u003eReaders 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.\u003c\/p\u003e\n\u003cp\u003ePacked with practical insights and future-ready strategies, this book equips readers to turn artificial intelligence from headline hype into competitive advantage.\u003c\/p\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003cp\u003e\u003cspan style=\"background-color: white; color: black;\"\u003eDr. 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 \u003c\/span\u003e\u003cem style=\"background-color: white; color: black;\"\u003eFinancial Services Review, Journal of Financial Therapy, Journal of Financial Counseling and Planning, \u003c\/em\u003e\u003cspan style=\"background-color: white; color: black;\"\u003eand \u003c\/span\u003e\u003cem style=\"background-color: white; color: black;\"\u003eJournal of Consumer Affairs\u003c\/em\u003e\u003cspan style=\"background-color: white; color: black;\"\u003e. \u003c\/span\u003e\u003c\/p\u003e\r\n\u003cp\u003e\u003cspan style=\"background-color: white; color: black;\"\u003eEunchan 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.\u003c\/span\u003e\u003c\/p\u003e\r\n\u003cp\u003e\u003cspan style=\"background-color: white; color: black;\"\u003eDr. 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.\u003c\/span\u003e\u003c\/p\u003e\n\u003c\/div\u003e\u003cbr\u003e\u003ctable\u003e\n\u003ctr\u003e\n\u003ctd\u003ePublication Date: \u003c\/td\u003e\n\u003ctd\u003e30 November 2026\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePublisher: \u003c\/td\u003e\n\u003ctd\u003eSpringer Nature Singapore\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eImprint: \u003c\/td\u003e\n\u003ctd\u003eSpringer\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eISBN-13: \u003c\/td\u003e\n\u003ctd\u003e9789819232147\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eFormat: \u003c\/td\u003e\n\u003ctd\u003eHardback\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e","brand":"Springer Nature Singapore","offers":[{"title":"Default Title","offer_id":50591097454732,"sku":"9789819232147","price":53.99,"currency_code":"USD","in_stock":true}],"url":"https:\/\/fh90cf-fv.myshopify.com\/products\/9789819232147","provider":"Late Knight Books and Services, LLC","version":"1.0","type":"link"}