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Data Science and Risk Analytics in Finance and Insurance book cover

1st Edition

Data Science and Risk Analytics in Finance and Insurance

By Tze Leung Lai, Haipeng Xing Copyright 2025
380 Pages 36 B/W Illustrations
by CRC Press

380 Pages 36 B/W Illustrations
by CRC Press
Also available as eBook on:
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... Read more

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

Biography

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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