Thesis Abstract

Abstract
The insurance industry faces significant challenges in detecting fraudulent activities, particularly in the realm of insurance claims. Fraudulent claims not only result in financial losses for insurance companies but also lead to increased premiums for honest policyholders. In response to this issue, this research project focuses on the development of a Fraud Detection System for Insurance Claims using Machine Learning Techniques. The primary objective of this study is to design and implement a system that can effectively identify and prevent fraudulent insurance claims through the application of advanced machine learning algorithms. The research begins with a comprehensive review of existing literature on fraud detection in the insurance industry, examining the various techniques and methodologies that have been employed to address this problem. This literature review highlights the importance of leveraging machine learning algorithms such as decision trees, random forests, and neural networks for fraud detection purposes. The methodology chapter outlines the research design and data collection process for the project. It discusses the selection of relevant datasets, data preprocessing techniques, feature selection methods, and the implementation of machine learning models for fraud detection. The chapter also details the evaluation metrics that will be used to assess the performance of the Fraud Detection System. The findings chapter presents the results of the experimental evaluation of the developed system. It discusses the accuracy, precision, recall, and F1-score of the machine learning models in detecting fraudulent insurance claims. The chapter also explores the impact of various factors, such as the size of the dataset and the choice of features, on the performance of the system. In the conclusion and summary chapter, the key findings and contributions of the research are summarized. The implications of the study for the insurance industry are discussed, along with recommendations for future research in the field of fraud detection using machine learning techniques. Overall, this research project provides valuable insights into the potential of machine learning for enhancing fraud detection in the insurance sector and offers a practical solution for mitigating the risks associated with fraudulent insurance claims.

Thesis Overview