Thesis Abstract

Abstract
Health insurance fraud poses a significant challenge to the healthcare industry, leading to financial losses, compromised patient care, and a decrease in overall trust in the system. As such, there is a pressing need for effective fraud detection mechanisms to mitigate these adverse effects. This thesis explores the application of machine learning techniques in detecting and preventing health insurance fraud. Chapter 1 provides an introduction to the research topic, discussing the background of the study, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definitions of key terms. Chapter 2 presents a comprehensive literature review on health insurance fraud, machine learning algorithms, and existing fraud detection methods. Chapter 3 details the research methodology, including data collection techniques, feature selection, model development, training and testing procedures, and evaluation metrics. The chapter also discusses ethical considerations and potential limitations of the methodology. In Chapter 4, the findings of the study are presented and analyzed in detail. The performance of various machine learning models in detecting health insurance fraud is compared, and the factors influencing their effectiveness are explored. The chapter also discusses practical implications and recommendations for implementing fraud detection systems in real-world healthcare settings. Finally, Chapter 5 summarizes the key findings of the study and offers conclusions based on the results obtained. The implications of the research findings for healthcare providers, insurers, policymakers, and other stakeholders are discussed. Recommendations for future research directions in the field of health insurance fraud detection using machine learning are also provided. Overall, this thesis contributes to the growing body of knowledge on health insurance fraud detection by demonstrating the potential of machine learning algorithms in improving fraud detection accuracy and efficiency. The findings of this study have important implications for enhancing the integrity and sustainability of the healthcare system by combatting fraudulent activities effectively.

Thesis Overview