Thesis Abstract

Abstract
The rise in fraudulent activities within the insurance industry has prompted the need for advanced techniques to detect, prevent, and mitigate such occurrences. This study focuses on the analysis of claim fraud detection in insurance companies using machine learning algorithms. The aim is to develop a robust system that can effectively identify fraudulent claims, thereby reducing financial losses and maintaining the integrity of insurance operations. Chapter One provides an introduction to the research topic, including the background of the study, problem statement, objectives, limitations, scope, significance, and the structure of the thesis. The chapter also includes definitions of key terms related to claim fraud detection and machine learning algorithms. Chapter Two presents a comprehensive literature review on claim fraud detection, machine learning algorithms, and their applications in the insurance industry. The review covers various studies and approaches used to address fraud detection challenges, highlighting the importance of leveraging machine learning techniques for enhanced fraud detection capabilities. Chapter Three outlines the research methodology employed in this study, including data collection methods, dataset preparation, feature selection, model development, and evaluation metrics. The chapter also discusses the implementation of machine learning algorithms such as logistic regression, decision trees, and neural networks for fraud detection. Chapter Four presents the findings of the study, showcasing the performance of different machine learning algorithms in detecting fraudulent insurance claims. The chapter includes a detailed analysis of the results obtained, highlighting the strengths and limitations of each algorithm in terms of accuracy, precision, recall, and F1-score. Chapter Five provides a conclusion and summary of the project thesis, summarizing the key findings, implications, and recommendations for future research. The chapter emphasizes the significance of using machine learning algorithms for claim fraud detection in insurance companies and discusses the potential impact of the study on the industry. In conclusion, this thesis contributes to the ongoing efforts to combat claim fraud in the insurance sector by leveraging machine learning algorithms for enhanced detection capabilities. The study highlights the importance of proactive fraud detection measures to safeguard the financial interests of insurance companies and maintain trust among policyholders. Through a systematic analysis of claim fraud detection using machine learning algorithms, this research aims to provide valuable insights and practical solutions to address the evolving challenges of fraud within the insurance industry.

Thesis Overview