Thesis Abstract

Abstract
Insurance fraud poses a significant threat to the financial stability and integrity of insurance companies, as well as the overall insurance industry. In recent years, the rise of sophisticated fraudulent activities has necessitated the development of advanced techniques to detect and prevent fraudulent insurance claims. This thesis focuses on the application of predictive modeling techniques for the detection of insurance claim fraud. The primary objective is to develop a predictive model that can effectively identify potentially fraudulent insurance claims, thereby enabling insurance companies to mitigate financial losses and maintain trust with their policyholders. Chapter One provides an introduction to the research topic, outlining the background of the study, the problem statement, objectives of the study, limitations, scope, significance of the study, structure of the thesis, and definition of key terms. The literature review in Chapter Two examines existing research on insurance claim fraud detection, exploring various predictive modeling techniques and methodologies employed in similar studies. Chapter Three presents the research methodology, detailing the data collection process, feature selection, model development, and evaluation metrics used to assess the performance of the predictive model. Chapter Four discusses the findings of the study, presenting the results of the predictive modeling approach in detecting insurance claim fraud. The chapter also analyzes the performance of the model, including its accuracy, precision, recall, and F1-score. Additionally, the chapter provides insights into the key features that contribute most significantly to the detection of fraudulent claims. Finally, Chapter Five offers a comprehensive conclusion and summary of the thesis, highlighting the key findings, implications for the insurance industry, and recommendations for future research. Overall, this thesis contributes to the field of insurance fraud detection by demonstrating the effectiveness of predictive modeling techniques in identifying fraudulent insurance claims. The findings of this study have practical implications for insurance companies seeking to enhance their fraud detection capabilities and protect their financial interests. By leveraging predictive modeling, insurance companies can proactively identify fraudulent activities, reduce losses, and safeguard the trust and confidence of policyholders.

Thesis Overview