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.

Thesis Overview

The project titled "Development of a Machine Learning Model for Predicting Insurance Claim Fraud" aims to address the critical issue of insurance claim fraud through the application of advanced machine learning techniques. Insurance claim fraud poses a significant challenge for insurance companies, leading to financial losses and undermining trust in the industry. By leveraging the power of machine learning, this research seeks to develop a predictive model that can effectively identify fraudulent claims, thereby enabling insurance companies to take proactive measures to mitigate fraud risk. The research will begin with a comprehensive review of existing literature on insurance claim fraud detection, machine learning algorithms, and related methodologies. This literature review will provide a solid foundation for understanding the current state of research in the field and identifying gaps that the proposed study aims to fill. The methodology section of the research will outline the data collection process, feature selection techniques, model development, and evaluation strategies. Various machine learning algorithms, such as logistic regression, decision trees, random forests, and neural networks, will be explored and compared to determine the most effective approach for predicting insurance claim fraud. The findings of the study will be presented in detail, highlighting the performance of the developed machine learning model in detecting fraudulent insurance claims. The discussion will delve into the factors that influence the accuracy of the model, potential challenges encountered during the research process, and implications for the insurance industry. In conclusion, the research will summarize the key findings, implications, and contributions to the field of insurance claim fraud detection. The study will also offer recommendations for future research directions and practical applications of the developed machine learning model in real-world insurance settings. Ultimately, the project aims to enhance fraud detection capabilities within the insurance sector, thereby improving operational efficiency and reducing financial losses associated with fraudulent claims.