Data Science and Risk Analytics in Finance and Insurance

This book presents statistics and data science methods for risk analytics in quantitative finance and insurance. The book offers a non-technical introduction to four key areas in financial technology: artificial intelligence, blockchain, cloud computing, and big data analytics.

Autorentext

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.

Inhalt

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