AI in Insurance - Silverfield, Emilia
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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…mehr

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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.