Thesis Abstract

Abstract
This thesis presents a comprehensive study on the application of machine learning algorithms for anomaly detection in insurance claims. The detection of anomalies in insurance claims plays a crucial role in identifying fraudulent activities, ensuring fair premium rates, and maintaining the financial stability of insurance companies. Traditional methods of anomaly detection rely on manual inspection and rule-based systems, which are often time-consuming and ineffective in handling the complex and evolving nature of fraudulent activities. Machine learning algorithms offer a promising solution by automating the detection process and adapting to new fraudulent patterns. The research begins with an introduction to the importance of anomaly detection in insurance claims and the limitations of existing methods. The background of the study provides an overview of the current challenges faced by insurance companies in detecting anomalies and the potential benefits of using machine learning algorithms. The problem statement highlights the need for more efficient and accurate anomaly detection techniques to combat insurance fraud effectively. The objectives of the study include developing and implementing machine learning models for anomaly detection, evaluating the performance of these models, and comparing them with traditional methods. The study also explores the limitations and challenges associated with using machine learning algorithms for anomaly detection in insurance claims. The scope of the study outlines the specific aspects of insurance claims that will be considered, such as fraudulent activities, false claims, and anomalies in claim patterns. The significance of the study lies in its potential to enhance fraud detection capabilities in the insurance industry, leading to improved risk management, reduced financial losses, and increased customer trust. The structure of the thesis is organized into five chapters, with each chapter focusing on specific aspects of the research. The definitions of key terms used throughout the thesis are provided to ensure clarity and understanding. Chapter two presents a detailed literature review of existing research on anomaly detection, machine learning algorithms, and their applications in the insurance industry. The review covers various techniques, methodologies, and case studies related to anomaly detection in insurance claims, providing a comprehensive overview of the current state of the art. Chapter three outlines the research methodology, including data collection, preprocessing, feature selection, model training, evaluation metrics, and validation techniques. The chapter also discusses the selection of machine learning algorithms, parameter tuning, and model optimization strategies to improve the performance of the anomaly detection models. Chapter four presents the findings of the study, including the performance metrics of the developed machine learning models, comparison with traditional methods, and insights into the effectiveness of different algorithms in detecting anomalies in insurance claims. The chapter also discusses the implications of the findings and their practical applications in real-world scenarios. Chapter five concludes the thesis by summarizing the key findings, highlighting the contributions of the study, and discussing future research directions. The conclusion emphasizes the potential of machine learning algorithms to revolutionize anomaly detection in insurance claims and addresses the implications for the insurance industry. In conclusion, this thesis contributes to the advancement of anomaly detection techniques in insurance claims through the application of machine learning algorithms. The research findings provide valuable insights into the effectiveness of these algorithms in detecting anomalies, highlighting their potential to enhance fraud detection capabilities and improve the overall efficiency of insurance claim processing.

Thesis Overview