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

By: Tze Leung Lai and Haipeng Xing

Sign Up Now! Already a Member? Log In
You must be logged into Bookshare to access this title. Learn about membership options, or view our freely available titles.

Synopsis: 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.

Book Quality:: Publisher Quality
Book Size:: 350 Pages
ISBN-13:: 9781351643252
Related ISBNs:: 9781351633826, 9781439839492, 9781315117041, 9781439839485
Publisher:: CRC Press
Date of Addition:: 10/02/24
Copyrighted By:: Taylor & Francis Group, LLC
Adult content:: No
Language:: English
Has Image Descriptions:: No
Categories:: Nonfiction, Reference, Business and Finance, Mathematics and Statistics
Submitted By:: Bookshare Staff
Usage Restrictions:: This is a copyrighted book.