BIBFRAME Work

Title

Data science and risk analytics in finance and insurance

Type

• Text
• Monograph

Contribution

• Lai, T. L. (author)
• Xing, Haipeng (author)

Subject

• Insurance--Data processing (LCSH)
• Finance--Mathematical models (LCSH)
• Finance--Statistical methods (LCSH)

Language

• English

Illustrative Content

• illustrations

Classification

• LCC: HG106 .L35 2025 (Assigner: dlc) (Status: used by assigner)
• DDC: 332.072/7 (Assigner: dlc)

Content

• text

Supplementary content

• bibliography
• index

Summary

"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"-- Provided by publisher.

Authorized Access Point

Lai, T. L. Data science and risk analytics in finance and insurance

Admin Metadata

• Date: 2026-09-11T22:44:03.784672+00:00
• Agent: cbc
• Status: c
• Derived from: 23705649

Admin Metadata

• Date: 2026-09-11
• Cataloger id: Unknown
• Description level: http://id.loc.gov/ontologies/bibframe-2-6-0/
• Description language: eng
• Description authentication: pcc