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Data Science and Risk Analytics in Finance and Insurance: Financial Models and Statistical Methods
Author(s): Tze Leung Lai (Author), Haipeng Xing (Author)
- Publisher: CRC Press
- Publication Date: 2 Oct. 2024
- Edition: 1st
- Language: English
- Print length: 366 pages
- ISBN-10: 1439839484
- ISBN-13: 9781439839485
Book Description
This book presents statistics and data science methods for risk analytics in quantitative finance and insurance. Part I covers the background, financial models, and data analytical methods for market risk, credit risk, and operational risk in financial instruments, as well as models of risk premium and insolvency in insurance contracts. Part II provides an overview of machine learning (including supervised, unsupervised, and reinforcement learning), Monte Carlo simulation, and sequential analysis techniques for risk analytics. In Part III, the book offers a non-technical introduction to four key areas in financial technology: artificial intelligence, blockchain, cloud computing, and big data analytics.
Key Features:
- Provides a comprehensive and in-depth overview of data science methods for financial and insurance risks.
- Unravels bandits, Markov decision processes, reinforcement learning, and their interconnections.
- Promotes sequential surveillance and predictive analytics for abrupt changes in risk factors.
- Introduces the ABCDs of FinTech: Artificial intelligence, blockchain, cloud computing, and big data analytics.
- Includes supplements and exercises to facilitate deeper comprehension.
Editorial Reviews
Review
“Overall, Data Science and Risk Analytics in Finance and Insurance is a well-executed and substantial book. It combines strong theoretical foundations with practical relevance and computational techniques. The breadth of topics covered, the clarity of exposition and the integration of classical and contemporary approaches make this work a significant contribution to the literature. The book deserves a wide readership among students, researchers and practitioners interested in quantitative finance and insurance analytics. It is likely to serve as an important reference work and a useful graduate-level textbook in the years ahead.”
-Svetlozar Rachev in the International Statistical Review, 2026.
About the Author
Tze Leung Lai is the Ray Lyman Wilbur Professor and Professor of Statistics at Stanford University. He received the COPSS Presidents’ Award in 1983. He has published extensively on sequential statistical analysis and a wide range of applications in the biomedical sciences, engineering, and finance.
Haipeng Xing is a Professor of Applied Mathematics and Statistics at State University of New York, Stony Brook. His research interests include sequential statistical methods and its applications, econometrics, quantitative finance, and recursive methods in macroeconomics.
