Development of a Machine Learning Model for Predicting Insurance Claim Fraud

Thesis Abstract

Abstract
The insurance industry faces significant challenges in detecting and preventing fraudulent insurance claims, which result in substantial financial losses each year. This thesis focuses on the development of a machine learning model to enhance the prediction of insurance claim fraud. The primary objective of this research is to leverage advanced machine learning algorithms to accurately identify fraudulent insurance claims, thereby enabling insurance companies to mitigate risks and improve operational efficiency. Chapter One provides an introduction to the research topic, including the background of the study, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definition of key terms. The literature review presented in Chapter Two examines existing studies related to insurance fraud detection, machine learning algorithms, and fraud prediction models. The chapter highlights the gaps in the current literature and identifies the need for advanced predictive models in the insurance industry. Chapter Three outlines the research methodology employed in this study, including data collection methods, data preprocessing techniques, feature selection, model development, and model evaluation. The chapter also discusses the ethical considerations associated with using machine learning for fraud detection in the insurance sector. The findings of the research are presented and discussed in Chapter Four, where the performance of the developed machine learning model in predicting insurance claim fraud is evaluated and compared with existing approaches. The results of the study demonstrate the effectiveness of the proposed machine learning model in accurately identifying fraudulent insurance claims. The model achieves high levels of precision, recall, and overall accuracy, outperforming traditional rule-based fraud detection systems. The implications of these findings for the insurance industry are discussed, highlighting the potential benefits of implementing advanced machine learning solutions for fraud prevention. In the final chapter, Chapter Five, the conclusions drawn from the research are summarized, and recommendations for future research and practical applications are provided. The study contributes to the body of knowledge on insurance fraud detection by showcasing the utility of machine learning techniques in enhancing fraud prediction accuracy. Overall, this thesis underscores the importance of leveraging advanced technologies to combat fraud in the insurance sector and offers valuable insights for industry practitioners and researchers alike.