Thesis Abstract

Abstract
Fraudulent activities in insurance claims pose significant challenges to insurance companies, leading to financial losses and decreased trust among stakeholders. To combat this issue, the application of machine learning algorithms for fraud detection has gained substantial attention in recent years. This thesis presents a comprehensive analysis of machine learning algorithms for fraud detection in insurance claims, with a focus on their effectiveness, efficiency, and practical implications. The study begins with an overview of the current landscape of insurance fraud, highlighting the prevalence of fraudulent activities and the need for advanced detection mechanisms. A review of existing literature on machine learning algorithms in fraud detection provides insights into the various approaches and techniques utilized in this domain. The research methodology section outlines the process followed in evaluating the performance of different machine learning algorithms for fraud detection. Data collection, preprocessing, feature engineering, model selection, and evaluation metrics are discussed in detail, providing a systematic framework for conducting the study. Chapter four presents the findings of the study, comparing the performance of popular machine learning algorithms such as logistic regression, random forest, support vector machines, and neural networks in detecting insurance fraud. The results highlight the strengths and weaknesses of each algorithm, shedding light on their applicability in real-world scenarios. The conclusion summarizes the key findings of the study, emphasizing the importance of leveraging machine learning algorithms for fraud detection in insurance claims. The implications of the research findings for insurance companies, regulators, and policy-makers are discussed, highlighting the potential benefits of implementing advanced fraud detection systems. Overall, this thesis contributes to the existing body of knowledge on fraud detection in insurance claims by providing a comprehensive analysis of machine learning algorithms. The findings of this study have practical implications for improving fraud detection processes in the insurance industry, ultimately leading to enhanced efficiency, reduced financial losses, and increased trust among stakeholders.

Thesis Overview