Thesis Abstract

Abstract
The insurance industry is constantly facing challenges related to fraudulent claims, which not only lead to financial losses but also undermine the trust between insurers and policyholders. In recent years, the advancement of machine learning techniques has provided a promising solution to detect and prevent insurance claims fraud effectively. This thesis explores the Application of Machine Learning in Predicting Insurance Claims Fraud, aiming to develop a predictive model that can accurately identify fraudulent claims and improve fraud detection mechanisms within insurance companies. The research begins with a comprehensive introduction that sets the context for the study, providing background information on insurance fraud, the significance of the problem, and the objectives of the research. The limitations and scope of the study are also outlined to clarify the boundaries and focus of the investigation. Moreover, the structure of the thesis is presented to guide readers through the subsequent chapters, and key terms related to the research topic are defined to ensure clarity and understanding. Chapter two delves into a detailed literature review that examines existing studies, methodologies, and findings related to machine learning applications in fraud detection within the insurance sector. This chapter provides insights into the current state of research, identifies gaps in the literature, and establishes a theoretical foundation for the study. Chapter three focuses on the research methodology employed in this study, including data collection, preprocessing, feature selection, model development, and performance evaluation. The chapter outlines the steps taken to train and validate the predictive model, ensuring its reliability and accuracy in detecting fraudulent insurance claims. Chapter four presents an in-depth discussion of the findings obtained from the application of machine learning techniques in predicting insurance claims fraud. The analysis of results, model performance metrics, and comparison with existing approaches are discussed to highlight the effectiveness and potential implications of the developed predictive model. Finally, chapter five presents the conclusion and summary of the thesis, summarizing the key findings, contributions, and implications of the research. Recommendations for future research directions and practical applications of the predictive model in real-world insurance settings are also provided to guide further advancements in fraud detection and prevention strategies. In conclusion, the Application of Machine Learning in Predicting Insurance Claims Fraud offers a valuable contribution to the insurance industry by introducing an innovative approach to enhancing fraud detection capabilities. The research findings provide insights into the potential benefits of leveraging machine learning algorithms for improving the efficiency and accuracy of fraud detection processes, ultimately leading to cost savings and enhanced security for insurance companies and policyholders.

Thesis Overview