Data Science and Risk Analytics in Finance and Insurance (Chapman and Hall/CRC Financial Mathematics Series)

by: Tze Leung Lai (Author),Haipeng Xing(Author)

Publisher: CRC Press

Edition: 1st

Publication Date: 2024/10/2

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 leaing (including supervised, unsupervised, and reinforcement leaing), 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 leaing, 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.

About the Author

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 leaing (including supervised, unsupervised, and reinforcement leaing), 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 leaing, 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.

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