Thesis Abstract

Abstract
This thesis explores the implementation of machine learning algorithms for fraud detection in insurance claims. The insurance industry faces significant challenges in detecting fraudulent activities, leading to substantial financial losses. Machine learning techniques offer promising solutions to enhance fraud detection capabilities by analyzing vast amounts of data to identify patterns indicative of fraudulent behavior. The research aims to develop and evaluate machine learning models that can effectively detect and prevent insurance fraud. Chapter one provides an introduction to the research topic, discussing the background of the study, the problem statement, objectives, limitations, scope, significance, and the structure of the thesis. The chapter also includes definitions of key terms relevant to the study. Chapter two presents a comprehensive literature review covering ten key areas related to fraud detection in insurance claims. The literature review examines existing research, methodologies, and technologies used in fraud detection within the insurance sector. Chapter three outlines the research methodology, detailing the research design, data collection methods, data preprocessing techniques, feature selection, model development, and evaluation metrics. The chapter also discusses the ethical considerations involved in handling sensitive insurance data for fraud detection purposes. Chapter four presents an in-depth discussion of the findings obtained from implementing machine learning algorithms for fraud detection in insurance claims. The chapter analyzes the performance of various machine learning models in detecting fraudulent activities and discusses the implications of the results. The conclusion and summary in chapter five provide a comprehensive overview of the research findings, highlighting the effectiveness of machine learning algorithms in enhancing fraud detection in insurance claims. The chapter discusses the implications of the research outcomes, recommendations for future research, and the practical implications for the insurance industry. Overall, this thesis contributes to the growing body of knowledge on utilizing machine learning for fraud detection in insurance, offering valuable insights and practical recommendations for improving fraud detection capabilities in the insurance sector.

Thesis Overview