Thesis Abstract

Abstract
The insurance industry is facing significant challenges due to the increasing occurrences of fraudulent activities in insurance claims. To address this issue, the development of advanced technologies such as machine learning models has gained considerable attention for improving fraud detection processes. This thesis focuses on the development of a machine learning model specifically tailored for fraud detection in insurance claims. The primary objective of this research is to enhance the accuracy and efficiency of fraud detection mechanisms within the insurance sector. The study begins with an introduction that highlights the background of the research, the problem statement, objectives, limitations, scope, significance, and the structure of the thesis. A thorough review of existing literature is conducted in Chapter Two, which encompasses ten key items related to fraud detection in insurance claims. This literature review provides a comprehensive understanding of the current state-of-the-art techniques, challenges, and opportunities in the field of fraud detection using machine learning algorithms. Chapter Three outlines the research methodology employed in this study, including data collection methods, data preprocessing techniques, feature selection processes, model development, performance evaluation metrics, and validation procedures. The chapter also discusses ethical considerations and potential biases that may arise during the research process. In Chapter Four, the findings of the study are extensively discussed, highlighting the effectiveness of the developed machine learning model in detecting fraudulent insurance claims. The chapter delves into the evaluation of model performance, comparison with existing approaches, and the identification of key insights gained from the analysis of fraud detection results. Finally, Chapter Five provides a comprehensive conclusion and summary of the project thesis. The study concludes by emphasizing the significance of the developed machine learning model for enhancing fraud detection in insurance claims, along with recommendations for future research directions in this domain. Overall, this thesis contributes to the advancement of fraud detection techniques in the insurance industry through the innovative application of machine learning algorithms.

Thesis Overview