Thesis Abstract

Abstract
The insurance industry faces significant challenges in detecting and preventing fraud in insurance claims, resulting in substantial financial losses. To address this issue, this study explores the application of machine learning algorithms for fraud detection in insurance claims. The primary objective of this research is to develop a predictive model that can effectively identify fraudulent insurance claims, thereby enhancing the efficiency and accuracy of fraud detection processes within insurance companies. The study begins with a comprehensive review of existing literature on fraud detection in insurance, focusing on the limitations of traditional methods and the potential benefits of machine learning approaches. The literature review highlights various machine learning algorithms commonly used in fraud detection, such as logistic regression, decision trees, random forests, support vector machines, and neural networks. The research methodology section outlines the steps involved in developing the fraud detection model, including data collection, data preprocessing, feature selection, model training, and evaluation. The study utilizes a real-world insurance claims dataset to train and test the machine learning model, with a focus on optimizing performance metrics such as accuracy, precision, recall, and F1 score. The findings of the study demonstrate the effectiveness of machine learning algorithms in detecting fraudulent insurance claims, outperforming traditional rule-based systems and heuristic approaches. The model achieves high levels of accuracy and sensitivity, enabling insurance companies to identify potential fraud cases with greater precision and efficiency. The discussion of findings section provides a detailed analysis of the results, highlighting the strengths and limitations of the machine learning model. The study emphasizes the importance of continuous model evaluation and improvement to adapt to evolving fraud patterns and enhance overall detection capabilities. In conclusion, this research contributes to the ongoing efforts to combat insurance fraud by leveraging the power of machine learning algorithms. The developed fraud detection model offers a viable solution for insurance companies seeking to enhance their fraud detection capabilities and minimize financial losses associated with fraudulent claims. By integrating advanced analytics and machine learning techniques into existing fraud detection processes, insurance companies can achieve significant improvements in fraud detection accuracy and efficiency.

Thesis Overview