Thesis Abstract

Abstract
The insurance industry faces significant challenges in detecting and preventing fraudulent activities related to insurance claims. Fraudulent claims not only result in financial losses for insurance companies but also compromise the integrity of the insurance system. To address this issue, the use of predictive modeling techniques has gained traction as an effective tool for identifying fraudulent claims in a timely manner. This research aims to develop and evaluate a predictive modeling approach specifically tailored for insurance claims fraud detection. The study begins with a comprehensive review of the existing literature on insurance fraud, predictive modeling techniques, and their applications in fraud detection. The literature review highlights the importance of predictive modeling in enhancing fraud detection accuracy and efficiency in the insurance sector. The research methodology section outlines the data collection process, feature selection techniques, model development, and evaluation strategies employed in this study. The research methodology also includes a detailed explanation of the dataset used for training and testing the predictive model. The findings of the study are presented and discussed in detail in Chapter Four. The results demonstrate the effectiveness of the developed predictive model in identifying potentially fraudulent insurance claims. The discussion also explores the key factors influencing the accuracy and reliability of the predictive model. In conclusion, this research contributes to the field of insurance claims fraud detection by proposing a novel predictive modeling approach tailored to the specific requirements of the insurance industry. The study highlights the significance of leveraging advanced analytics and machine learning techniques to enhance fraud detection capabilities in insurance claims processing. Overall, this thesis provides valuable insights and practical recommendations for insurance companies seeking to improve their fraud detection systems and safeguard their financial interests. The findings of this research have implications for policy development, risk management practices, and operational strategies in the insurance sector.

Thesis Overview