ISBN: 9781439839485 - data science and risk analytics in finance and insurance: financial models and statistical methods (21 risultati)

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Condizione: As New. Unread book in perfect condition.

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Hardback. Condizione: New. New copy - Usually dispatched within 4 working days.

Condizione: New. pp. 350 1st Edition.

Condizione: New. pp. 350.

Condizione: New. In English.

Hardback. Condizione: New. 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.…

Hardcover. Condizione: Brand New. 1st edition. 350 pages. 6.14x1.24x9.21 inches. In Stock.

Gebundene Ausgabe. Condizione: Neu. 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. Sprache: Englisch Gewicht in Gramm: 807.…

Hardback. Condizione: New. 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.…

Condizione: New. pp. 350 This item is printed on demand.

HRD. Condizione: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Gebunden. Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Tze Leung Lai is a professor of statistics at Stanford University in California. Haipeng Xing is an assistant professor of applied mathematics and statistics at the State University of New York, Stony Brook.Tze Leung Lai is the R.…

HRD. Condizione: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Buch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 464 pp. Englisch. …

Hardback. Condizione: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days.

Buch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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.…