Thesis Abstract

Abstract
The insurance industry faces significant challenges in detecting and preventing fraudulent activities within insurance claims. Fraudulent claims not only result in financial losses for insurance companies but also undermine the trust and integrity of the entire insurance system. In response to these challenges, this study aims to explore the application of machine learning algorithms for fraud detection in insurance claims. Chapter 1 provides an introduction to the research topic, presenting the background of the study, the problem statement, objectives, limitations, scope, significance of the study, structure of the thesis, and definitions of key terms. Chapter 2 presents a comprehensive literature review consisting of ten key areas related to fraud detection in insurance, machine learning algorithms, and previous research studies in the field. Chapter 3 outlines the research methodology employed in this study, including data collection methods, data preprocessing techniques, feature selection processes, and the implementation of machine learning models for fraud detection. The chapter also discusses the evaluation metrics used to measure the performance of the models and the ethical considerations involved in handling sensitive insurance data. In Chapter 4, the findings of the study are elaborated upon, presenting the results of applying various machine learning algorithms to detect fraudulent insurance claims. The chapter discusses the accuracy, precision, recall, and F1-score of each model, highlighting their strengths and weaknesses in identifying fraudulent activities. Additionally, the chapter explores the factors influencing the performance of the models and provides insights into potential improvements for future research in fraud detection. Chapter 5 concludes the thesis by summarizing the key findings, discussing the implications of the research results for the insurance industry, and proposing recommendations for enhancing fraud detection practices using machine learning algorithms. The chapter also reflects on the limitations of the study and suggests avenues for further research to advance the field of fraud detection in insurance claims. Overall, this thesis contributes to the growing body of knowledge on fraud detection in insurance claims by demonstrating the effectiveness of machine learning algorithms in improving fraud detection accuracy and efficiency. The findings of this study have practical implications for insurance companies seeking to enhance their fraud detection capabilities and reduce financial losses associated with fraudulent claims.

Thesis Overview