Development of a Machine Learning Algorithm for Fraud Detection in Insurance Claims

Thesis Abstract

Abstract
The rise in fraudulent activities within the insurance industry has become a significant concern for both insurance companies and policyholders. To address this challenge, the development of effective fraud detection systems is crucial. This thesis focuses on the development of a machine learning algorithm specifically tailored for detecting fraud in insurance claims. The proposed algorithm aims to enhance the accuracy and efficiency of fraud detection processes, ultimately leading to cost savings and improved customer trust. Chapter 1 provides an introduction to the research topic, outlining the background of the study, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definition of key terms. The literature review in Chapter 2 explores existing research on fraud detection in insurance claims, highlighting key concepts, methodologies, and technologies used in similar studies. Chapter 3 details the research methodology employed in this study, encompassing data collection, preprocessing, feature selection, model training, and evaluation techniques. The chapter also discusses the selection criteria for machine learning algorithms and the validation process for the proposed fraud detection system. In Chapter 4, the findings of the research are presented and analyzed in detail. The performance metrics of the developed machine learning algorithm are evaluated, including accuracy, precision, recall, and F1 score. The chapter also discusses the strengths and limitations of the algorithm, as well as potential areas for future research and improvement. Finally, Chapter 5 concludes the thesis by summarizing the key findings, implications, and contributions of the research. The conclusion reflects on the effectiveness of the developed machine learning algorithm for fraud detection in insurance claims and its potential impact on the industry. Recommendations for further research and practical applications are also provided. Overall, this thesis contributes to the field of insurance fraud detection by proposing a novel machine learning algorithm tailored to the specific needs of insurance companies. The research outcomes are expected to enhance fraud detection capabilities, reduce financial losses due to fraudulent claims, and improve the overall integrity of the insurance industry.