Thesis Abstract

Abstract
This thesis investigates the application of machine learning algorithms for fraud detection in insurance claims processing. The insurance industry faces significant challenges related to fraudulent activities, which not only result in financial losses but also undermine the trust and integrity of the system. Machine learning, with its ability to detect patterns and anomalies in large datasets, offers a promising approach to enhancing fraud detection in insurance claims. The research begins with a comprehensive review of the existing literature on fraud detection in insurance claims. This literature review covers various machine learning algorithms, such as decision trees, random forests, support vector machines, and neural networks, that have been utilized in fraud detection applications. The review also explores different fraud detection techniques, challenges, and best practices in the insurance industry. Following the literature review, the research methodology section outlines the approach taken in this study. The methodology includes data collection procedures, feature selection techniques, model training and evaluation processes, and validation methods. Data preprocessing steps, such as data cleaning, normalization, and feature engineering, are also detailed to ensure the quality and reliability of the analysis. The empirical findings from applying machine learning algorithms to a real-world insurance claims dataset are presented and discussed in Chapter Four. The performance of various algorithms in terms of accuracy, precision, recall, and F1 score is evaluated to determine their effectiveness in fraud detection. The results highlight the strengths and limitations of each algorithm and provide insights into their practical implications for insurance companies. In the conclusion and summary chapter, the key findings of the research are summarized, and recommendations for future research are provided. The study contributes to the growing body of knowledge on fraud detection in insurance claims and offers practical implications for insurance companies seeking to enhance their fraud detection capabilities using machine learning algorithms. Overall, this thesis contributes to the understanding of how machine learning algorithms can be effectively utilized for fraud detection in insurance claims. By leveraging advanced analytics and predictive modeling techniques, insurance companies can proactively identify and prevent fraudulent activities, thereby improving operational efficiency and customer trust in the insurance industry.

Thesis Overview