Data science and risk analytics in finance and insurance

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

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.

(https://www.routledge.com/Data-Science-and-Risk-Analytics-in-Finance-and-Insurance/Lai-Xing/p/book/9781439839485?srsltid=AfmBOoqJsKMv656n8JKO9QrJhtKw9WGLr_YFDGsHU4CoWZjI4lLVDpA2)