Thesis Abstract

Abstract
The insurance industry faces significant challenges in detecting fraudulent activities related to insurance claims. Fraudulent claims not only lead to financial losses for insurance companies but also erode trust in the industry. To address this issue, the use of machine learning algorithms has gained prominence as an effective tool for fraud detection. This thesis explores the application of various machine learning algorithms for fraud detection in insurance claims. The research begins by providing an introduction to the topic, highlighting the background of the study, the problem statement, objectives, limitations, scope, significance, and the overall structure of the thesis. A detailed literature review in Chapter Two examines existing studies on fraud detection in insurance using machine learning algorithms. The review covers ten key areas, including the types of fraud, traditional fraud detection methods, and the advantages of machine learning in fraud detection. Chapter Three outlines the research methodology, detailing the research design, data collection methods, sampling techniques, data preprocessing, model selection, evaluation metrics, and validation procedures. The methodology section also discusses the ethical considerations and limitations of the study. Chapter Four presents a comprehensive discussion of the findings from applying various machine learning algorithms to detect fraud in insurance claims. The chapter analyzes the performance of each algorithm, identifies key factors influencing fraud detection accuracy, and discusses the implications of the results for the insurance industry. Finally, Chapter Five offers a conclusion and summary of the thesis, highlighting the key findings, implications for practice, and recommendations for future research. The study contributes to the growing body of knowledge on fraud detection in insurance claims and provides valuable insights for insurance companies seeking to enhance their fraud detection capabilities using machine learning algorithms.

Thesis Overview

The project titled "Analysis of Machine Learning Algorithms for Fraud Detection in Insurance Claims" focuses on leveraging machine learning algorithms to enhance fraud detection in the insurance industry. Fraud detection is a critical challenge for insurance companies as fraudulent activities can lead to significant financial losses. Traditional rule-based systems may not be effective in detecting sophisticated fraud schemes, hence the need to explore advanced technologies like machine learning. This research aims to investigate and analyze various machine learning algorithms to develop a robust fraud detection system specifically tailored for insurance claims. By harnessing the power of machine learning, this project seeks to improve the accuracy and efficiency of fraud detection processes, ultimately helping insurance companies minimize financial risks and protect their assets. The research will involve a comprehensive literature review to examine existing studies and methodologies related to fraud detection in insurance claims. By analyzing and synthesizing previous research findings, this study aims to identify gaps in the current literature and propose novel approaches to address these limitations. Furthermore, the project will outline a detailed research methodology that includes data collection, preprocessing, feature engineering, model selection, training, and evaluation. Various machine learning algorithms such as decision trees, random forests, support vector machines, and neural networks will be implemented and compared to determine their effectiveness in detecting insurance fraud. The findings of this research will be presented and discussed in chapter four, where the performance of different machine learning algorithms in fraud detection will be evaluated based on metrics such as accuracy, precision, recall, and F1 score. The discussion will also highlight the strengths and limitations of each algorithm and provide insights into the practical implications of the results. In conclusion, this research seeks to contribute to the ongoing efforts to combat insurance fraud by harnessing the capabilities of machine learning algorithms. By developing an effective fraud detection system, insurance companies can enhance their risk management strategies, improve operational efficiency, and safeguard their financial interests in an increasingly complex and competitive market landscape.