Thesis Abstract

Abstract
Insurance claim fraud is a significant issue that impacts both insurance companies and policyholders. The ability to detect fraudulent claims in a timely and accurate manner is crucial for minimizing financial losses and maintaining the integrity of the insurance industry. Predictive modeling has emerged as a powerful tool for identifying patterns and anomalies in large datasets, making it an ideal approach for fraud detection in insurance claims. This thesis explores the development and implementation of a predictive modeling framework for insurance claim fraud detection. The research begins with a comprehensive review of existing literature on fraud detection techniques, machine learning algorithms, and applications in the insurance industry. The literature review provides a foundation for understanding the current state of the art in fraud detection and highlights the gaps that this research aims to address. The methodology chapter outlines the steps taken to design and implement the predictive modeling framework. Data collection, preprocessing, feature selection, model training, and evaluation are detailed to demonstrate the systematic approach taken in developing the fraud detection system. The research methodology also includes a discussion of the dataset used, the selection of evaluation metrics, and the validation process to ensure the robustness and reliability of the predictive models. The findings chapter presents the results of the predictive modeling experiments conducted on the insurance claim dataset. The performance of different machine learning algorithms, including logistic regression, random forest, and neural networks, is evaluated and compared in terms of accuracy, precision, recall, and F1 score. The analysis of the results sheds light on the strengths and limitations of each algorithm and provides insights into the most effective approaches for detecting insurance claim fraud. The discussion chapter delves into the implications of the research findings and their relevance to the insurance industry. The challenges and opportunities in implementing predictive modeling for fraud detection are explored, along with recommendations for improving the accuracy and efficiency of the fraud detection system. The chapter also discusses the ethical considerations of using predictive modeling in insurance claim fraud detection and the potential impact on policyholders and insurers. In conclusion, this thesis contributes to the field of insurance claim fraud detection by providing a detailed analysis of the predictive modeling framework and its effectiveness in identifying fraudulent claims. The research highlights the importance of leveraging machine learning algorithms and big data analytics to enhance fraud detection capabilities in the insurance sector. The insights gained from this study can inform future research and practical applications aimed at combating insurance claim fraud and protecting the interests of stakeholders in the industry.

Thesis Overview