AI in Insurance. Este artículo no está disponible.
Idioma: inglés
Editorial: PublishDrive, 2025
- Tapa blanda
- Nuevo

Librería: PBShop.store US, Wood Dale, IL, Estados Unidos de AmericaPBShop.store US
Vendedor de IberLibro desde 7 de abril de 2005
Condición: Nuevo
EUR 13,23
Descripción del artículo del vendedor
New Book. Shipped from UK. Established seller since 2000.
N° de ref. del artículo L2-9798898603670
- Título
- AI in Insurance
- Autor
- Silverfield, Emilia
- Editorial
- PublishDrive
- Año de publicación
- 2025
- Estado
- New
- Encuadernación
- PAP
- Idioma
- inglés
- ISBN 13
- 9798898603670
- Peso del artículo
- 212 gramos
Transforming Risk, Operations, and Customer Experience
Artificial intelligence is revolutionizing the $1.3 trillion global insurance industry, from automated underwriting that processes applications in minutes to predictive analytics that forecast risks before they occur. Yet many insurance professionals struggle to understand how to harness these powerful technologies effectively.
This comprehensive guide demystifies AI applications across every aspect of insurance operations. Learn how machine learning enhances risk assessment accuracy, computer vision automates claims processing, and natural language processing transforms customer service. Discover practical frameworks for implementing AI-driven fraud detection, personalizing products at scale, and building data infrastructure that supports advanced analytics.
Key topics include:
Data architecture and governance for AI readiness
Machine learning techniques for underwriting and pricing
Automated claims processing and fraud detection
AI-powered customer experience enhancement
Risk profiling using IoT and behavioral data
Regulatory compliance and ethical AI deployment
Strategic implementation across health, property, commercial, and cyber insurance.
Written for insurance executives, underwriters, claims professionals, and technology leaders, this book provides actionable insights for navigating digital transformation while addressing critical considerations around bias, transparency, and regulatory compliance.
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