Data Science and Risk Analytics in Finance and Insurance
Author:   Tze Leung Lai (Stanford University, California, USA) ,  Haipeng Xing (SUNY, Stony Brook, New York, USA)
Publisher:   Taylor & Francis Inc
ISBN:  

9781439839485


Pages:   366
Publication Date:   02 October 2024
Format:   Hardback
Availability:   In Print   Availability explained
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Data Science and Risk Analytics in Finance and Insurance


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Author:   Tze Leung Lai (Stanford University, California, USA) ,  Haipeng Xing (SUNY, Stony Brook, New York, USA)
Publisher:   Taylor & Francis Inc
Imprint:   CRC Press Inc
Weight:   0.861kg
ISBN:  

9781439839485


ISBN 10:   1439839484
Pages:   366
Publication Date:   02 October 2024
Audience:   College/higher education ,  Professional and scholarly ,  Postgraduate, Research & Scholarly ,  Professional & Vocational
Format:   Hardback
Publisher's Status:   Active
Availability:   In Print   Availability explained
This item will be ordered in for you from one of our suppliers. Upon receipt, we will promptly dispatch it out to you. For in store availability, please contact us.

Table of Contents

Preface Part 1: Background and Basic Analytics 1. Risk management and regulation 2. Basic concepts and methods in risk management 3. Financial derivatives and their pricing theory 4. Insurance risk and credibility theory Part 2: Advanced Data and Risk Analytics 5. Supervised and unsupervised learning 6. Bandit, Markov decision process and reinforcement learning 7. Monte Carlo methods and rare event analytics 8. Surveillance and predictive analytics Part 3: Data and Risk Analytics in FinTech 9. FinTech ABCD and analytics Bibliography Index

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Author Information

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.

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