Data Science and Risk Analytics in Finance and Insurance - Lai, Tze Leung; Xing, Haipeng
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Following the recent financial crisis, risk management in financial institutions, particularly in banks, has attracted widespread attention and discussion. Novel modeling approaches and courses to educate future professionals in industry, government, and academia are of timely relevance. This book introduces an innovative concept and methodology developed by the authors: active risk management. It is suitable for graduate students in mathematical finance/financial engineering, economics, and statistics as well as for practitioners in the fields of finance and insurance. The book's website…mehr

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Produktbeschreibung
Following the recent financial crisis, risk management in financial institutions, particularly in banks, has attracted widespread attention and discussion. Novel modeling approaches and courses to educate future professionals in industry, government, and academia are of timely relevance. This book introduces an innovative concept and methodology developed by the authors: active risk management. It is suitable for graduate students in mathematical finance/financial engineering, economics, and statistics as well as for practitioners in the fields of finance and insurance. The book's website features the data sets used in the examples along with various exercises.
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.
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Autorenporträt
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.