Thesis Abstract

Abstract
The insurance industry plays a crucial role in mitigating financial risks for individuals and organizations. However, fraudulent activities in insurance claims pose significant challenges to the industry, leading to substantial financial losses. In response to this pressing issue, the application of machine learning algorithms for fraud detection in insurance claims has emerged as a promising solution. This thesis investigates the effectiveness of machine learning algorithms in detecting fraudulent activities in insurance claims and proposes a comprehensive framework for enhancing fraud detection accuracy and efficiency. The study begins with an in-depth exploration of the background of fraudulent activities in insurance claims, highlighting the detrimental impact of fraud on the industry and the need for advanced detection mechanisms. The problem statement identifies the current limitations of traditional fraud detection methods and underscores the importance of leveraging machine learning techniques to address these challenges effectively. The objectives of the study encompass evaluating the performance of various machine learning algorithms in detecting insurance fraud, optimizing the detection process through feature engineering and model tuning, and assessing the practical implications of implementing machine learning-based fraud detection systems in insurance companies. The limitations of the study are acknowledged, emphasizing the need for further research to refine and extend the proposed framework. The scope of the study focuses on analyzing historical insurance claims data to train and evaluate machine learning models for fraud detection. The significance of the study lies in its potential to enhance fraud detection accuracy, reduce financial losses for insurance companies, and improve overall trust and reliability in the insurance industry. The structure of the thesis is outlined, providing a roadmap for the subsequent chapters that delve into the literature review, research methodology, discussion of findings, and conclusion. The literature review chapter critically examines existing research on fraud detection in insurance claims, highlighting the strengths and limitations of different machine learning algorithms and methodologies. The research methodology chapter details the data collection process, feature selection techniques, model development procedures, and evaluation metrics employed in the study, ensuring methodological rigor and validity of the results. In the discussion of findings chapter, the performance of various machine learning algorithms in detecting insurance fraud is evaluated, and key insights into the factors influencing fraud detection accuracy are presented. The implications of the study findings for insurance companies and the broader industry are discussed, emphasizing the practical implications of adopting machine learning-based fraud detection systems. In conclusion, this thesis underscores the potential of machine learning algorithms in enhancing fraud detection in insurance claims and provides practical recommendations for implementing effective fraud detection systems. By leveraging advanced machine learning techniques, insurance companies can significantly improve their ability to detect and prevent fraudulent activities, ultimately safeguarding their financial interests and maintaining trust and integrity in the insurance industry.

Thesis Overview