Thesis Abstract

Abstract
The integration of artificial intelligence (AI) technologies in the insurance industry has brought about significant transformations, offering both opportunities and challenges for insurers. This thesis examines the impact of AI on the insurance sector, focusing on the opportunities it presents and the challenges it poses. Through an in-depth analysis of the current landscape, this study aims to provide valuable insights into how AI is reshaping the industry and what implications it has for insurers, customers, and other stakeholders. The research begins with an exploration of the background of AI in insurance, highlighting the evolution of AI technologies and their adoption in the industry. The problem statement underscores the need to understand the implications of AI for insurers and the broader insurance ecosystem. The objectives of the study are to assess the opportunities that AI offers for improving operational efficiency, enhancing customer experience, and enabling new business models, while also addressing the challenges related to data privacy, regulatory compliance, and ethical considerations. Despite the potential benefits of AI in insurance, there are limitations that need to be considered, such as data quality issues, implementation costs, and the need for upskilling the workforce. The scope of the study is defined to focus on key aspects of AI adoption in insurance, including underwriting, claims processing, customer service, and risk management. The significance of the study lies in its contribution to the existing body of knowledge on AI in insurance and its practical implications for insurers looking to leverage AI technologies effectively. The structure of the thesis is organized into five chapters. Chapter 1 provides an introduction to the topic, presents the background of the study, articulates the problem statement, outlines the objectives, discusses the limitations and scope of the study, highlights the significance of the research, and offers a roadmap for the thesis. Chapter 2 presents a comprehensive literature review on AI in insurance, covering key concepts, trends, challenges, and opportunities in the field. Chapter 3 details the research methodology employed in the study, including the research design, data collection methods, sampling techniques, and data analysis procedures. The chapter also discusses the ethical considerations and limitations of the research process. Chapter 4 presents the findings of the study, analyzing the opportunities and challenges of AI adoption in the insurance industry based on empirical evidence and case studies. Finally, Chapter 5 concludes the thesis by summarizing the key findings, discussing their implications, and offering recommendations for insurers and policymakers. The conclusion reflects on the potential future developments in AI and insurance and suggests areas for further research to advance our understanding of this evolving landscape. Overall, this thesis contributes to the ongoing discourse on AI in insurance, shedding light on the opportunities and challenges that AI presents for the industry and offering insights to help stakeholders navigate this transformative journey.

Thesis Overview