The Complete Compendium Of Digital Insurance Solutions Life Health Auto Property And Specialized Coverage In The Age Of Ai Automation And Intelligent Risk Management
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The Complete Compendium of Digital Insurance Solutions: Life, Health, Auto, Property, and Specialized Coverage in the Age of AI, Automation, and Intelligent Risk Management
Author: Lahari Pandiri
language: en
Publisher: Deep Science Publishing
Release Date: 2025-06-06
The global insurance landscape is undergoing a profound transformation—driven by artificial intelligence, automation, and data-centric innovation. The Complete Compendium of Digital Insurance Solutions: Life, Health, Auto, Property, and Specialized Coverage in the Age of AI, Automation, and Intelligent Risk Management offers a comprehensive guide to this rapidly evolving ecosystem, examining how cutting-edge technologies are redefining the way insurers assess risk, interact with customers, and deliver value. As digital disruption accelerates, traditional insurance models are being reshaped by AI-powered underwriting, predictive analytics, real-time claims processing, and hyper-personalized policy design. Whether it's streamlining auto insurance through telematics, enhancing life insurance underwriting with health data, or modernizing property coverage with drone and IoT-based assessments, intelligent automation is making insurance more responsive, efficient, and customer-focused than ever before. This compendium is intended for a diverse readership—insurance professionals, technology developers, policy regulators, academic researchers, and students—providing insights across the full spectrum of insurance verticals: life, health, auto, property, and niche products. It brings together a rich collection of frameworks, use cases, and future-facing strategies that highlight the integration of digital technologies with core insurance functions, from customer onboarding and fraud detection to compliance and claims adjudication. Beyond the technical and operational aspects, this book also addresses critical themes such as data privacy, algorithmic fairness, regulatory innovation, and the ethical deployment of AI in risk management. It emphasizes that digital transformation in insurance is not merely about adopting new tools—it’s about reimagining the role of insurance in a digital society. As insurers continue to embrace digital-first strategies, this work serves as both a practical reference and a strategic vision for building resilient, intelligent, and inclusive insurance systems that meet the needs of the modern world.
Ethical Considerations and Bias Detection in Artificial Intelligence/Machine Learning Applications
Author: Jayesh Rane
language: en
Publisher: Deep Science Publishing
Release Date: 2025-07-10
At a time when artificial intelligence (AI) and machine learning (ML) are used to make sensitive societal decisions such as the ones related to criminal justice, healthcare, finance, education, employment, algorithmic fairness and bias mitigation are among the most important but challenging issues at hand. The goal of this book is to provide a holistic view across various disciplines of the ethical base, detection methods, and technical measures for trustworthy AI systems. Starting from a solid foundation of statistical bias, transparency systems and fairness-aware ML models, this book methodically looks at state-of-the-art methodologies, where we highlight their shortcomings and introduce a unified model framework for detecting bias and transparent algorithms. Moving beyond technical diagnoses, it examines key sociotechnical and policy tools that are required to implement AI responsibly, providing guidance to researchers, engineers, policy makers, and organizational leaders. Literature review has been driven following the experimental case, the fairness trade-offs, intersectional bias, explainability and regulatory compliance are discussed in depth by the authors. This work underscores that fairness in automated decision-making systems depends not only on algorithmic accuracy, but also institutional will and stakeholder engagement. The chapters in this book function as both an academic primer and a resourceful handbook, transitioning readers through an ever-growing ethical AI terrain. Whether you are a data scientist building and deploying an algorithm that encourages ethical speech, or a regulator working to create and refine guidelines around such algorithms, this book provides you with both the tools and the understanding you need for ethical technology development and deployment.
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