Thesis Abstract

Abstract
The insurance industry faces significant challenges in detecting fraudulent claims, which can lead to substantial financial losses. Traditional methods of fraud detection are often manual, time-consuming, and prone to errors. In recent years, machine learning techniques have emerged as powerful tools for improving fraud detection accuracy and efficiency. This research focuses on developing a predictive modeling framework for insurance claim fraud detection using machine learning techniques. Chapter One provides an introduction to the research topic, including the background of the study, problem statement, objectives, limitations, scope, significance, and structure of the thesis. The chapter also includes definitions of key terms related to insurance fraud detection and machine learning. Chapter Two presents a comprehensive literature review on the use of machine learning techniques in fraud detection within the insurance industry. The review covers key concepts, methodologies, and best practices in predictive modeling for fraud detection, as well as relevant studies and research findings. Chapter Three outlines the research methodology employed in this study, including data collection, preprocessing, feature selection, model development, and evaluation. The chapter also discusses the selection of machine learning algorithms, model training, and performance evaluation metrics. Chapter Four presents a detailed discussion of the findings obtained from applying the predictive modeling framework to real-world insurance claim datasets. The chapter analyzes the performance of different machine learning algorithms in detecting fraudulent claims and compares the results with traditional fraud detection methods. Chapter Five summarizes the research findings, discusses the implications of the study, and provides recommendations for future research in the field of insurance claim fraud detection using machine learning techniques. The chapter concludes with a discussion of the contributions of this research and its potential impact on the insurance industry. Overall, this research contributes to the growing body of knowledge on predictive modeling for insurance claim fraud detection using machine learning techniques. By leveraging advanced machine learning algorithms, insurance companies can enhance their fraud detection capabilities, reduce financial losses, and improve overall operational efficiency.

Thesis Overview