Ethical Artificial Intelligence in the Insurance Industry

Balancing Efficiency, Fairness, and Risk
1. Auflage 2026
ISBN: 978-1-041-23688-7
Verlag: Taylor & Francis Ltd

Buch, Englisch, 308 Seiten, Format (B × H): 178 mm x 254 mm

The book provides an overview of the challenges and opportunities presented by AI across the insurance value chain. As insurers rapidly integrate machine learning, deep learning, and predictive analytics into underwriting, claims processing, fraud detection, and pricing, the need for robust ethical frameworks and responsible AI governance has become paramount. Algorithmic structures and data pipelines that shape modern insurance systems, that review potential sources of bias, opacity, and inequality are examined. The book addresses technical, legal, and organizational dimensions of ethical AI adoption—ranging from explainability and accountability mechanisms to data privacy, informed consent, and inclusion. The book serves as a foundational guide for developing AI systems in insurance that are not only efficient but also equitable and socially responsible. The book will be invaluable for professionals, scholars, data scientists, actuaries, and policymakers.

Key Features:

- Explores cutting-edge applications of AI across underwriting, claims processing, fraud detection, and dynamic pricing in the insurance industry.

- Reviews the latest advances in algorithmic fairness, explainability (XAI), and bias mitigation techniques tailored to insurance models.

- Analyzes global regulatory and ethical frameworks, including GDPR, AI Act, and sector-specific policies, shaping responsible AI adoption.

- Provides real-world case studies and technical insights into building accountable, transparent, and inclusive AI systems for insurers.

- Equips practitioners, data scientists, and policymakers with strategic tools to design, govern, and audit ethical AI in insurance operations.

Zielgruppe

Academic, Postgraduate, and Professional Practice & Development

Autoren/Hrsg.

Shafik, Wasswa

Fachgebiete

• Wirtschaftswissenschaften Finanzsektor & Finanzdienstleistungen Versicherungswirtschaft

Inhaltsverzeichnis

Preface. 1. The Ethical Imperative in AI-Driven Insurance: Foundations, Motivations, and Risks. 2. Architecting Intelligence: Understanding the AI Value Chain in the Insurance Ecosystem. 3. Navigating Legal, Regulatory, and Ethical Frameworks in Algorithmic Insurance Practices. 4. Algorithmic Underwriting: Addressing Bias, Transparency, and Data Equity in Risk Assessment. 5. AI-Enabled Claims Management: Automation, Fairness, and Human-AI Collaboration. 6. Combating Insurance Fraud with AI: Balancing Predictive Power and Ethical Constraints. 7. Dynamic Pricing and Personalization: Ethical Implications of Behavioral and Big Data in Premium Models. 8. Detecting and Mitigating Algorithmic Bias: Technical and Ethical Interventions in Insurance AI. 9. Explainable AI for Insurance: Enhancing Transparency, Accountability, and Client Trust. 10. Data Governance in Insurance AI: Ensuring Privacy, Consent, and Ethical Data Life Cycles. 11. Embedding Human Oversight in Automated Systems: Toward Accountable and Trustworthy AI. 12. Equity and Inclusion in Insurance AI: Expanding Access for Marginalized and Underserved Populations. 13. Global Perspectives on Ethical Insurance AI: Comparative Case Studies and Regional Challenges. 14. Designing an Ethical AI Strategy for Insurers: Policies, Practices, and Organizational Transformation. 15. Toward a Human-Centric Future: Strategic Road Maps for Sustainable and Ethical AI Integration in Insurance. About the Author.

Über Autor(innen)

Wasswa Shafik (member, IEEE) is a computer scientist, an information technologist and educator, and a research director at the Dig Connectivity Research Laboratory (DCRLab), Kampala, Uganda. He received his Bachelor in Information Technology at Ndejje University, Luweero, Uganda, and his Master in Information Technology Engineering (Communication and Computer Networks Option) at Yazd University, Yazd, Islamic Republic of Iran. He further pursued his PhD in Digital Science (Computer Science) at the School of Digital Science, Universiti Brunei Darussalam, Brunei Darussalam. His research broadly examines, integrates, and focuses on developing computationally and statistically efficient models and algorithms to address complex questions in artificial intelligence and machine learning problems for a sustainable future. His specific research interests include applied artificial intelligence, smart agriculture, computer vision, ecological informatics, digital health and education, and sustainable computing. He has authored, edited, co-edited, and published hundreds of peer-reviewed books, technical papers, book sections, and numerous IEEE International Conferences and prestigious international journals. He has served as a reviewer of several international journals, Scopus, Compendex (Elsevier Engineering Index), and WoS international journals. He further served in different capacities as department support for mathematics for data science, advanced topics in computing, advanced algorithms, and system performance and evaluation. Prior to this, as a department fellow, he served as a researcher associate at the Intelligent Network Laboratory in Iran. He served in different capacities as a community data officer at the Programme for Accessible Health, Communication and Education, as a research associate and data manager at Population Services International, as a data manager and research assistant at the Socio-Economic Data Center, as a research lead at TechnoServe and as a Ag. chief executive officer at Asmaah Charity Organisation.