Thesis Abstract

Abstract
Insurance claim fraud poses a significant challenge to insurance companies, leading to substantial financial losses and undermining the trust of policyholders. In response to this issue, predictive modeling has emerged as a powerful tool for detecting and preventing fraudulent activities in the insurance industry. This thesis focuses on the development and implementation of a predictive modeling system for insurance claim fraud detection. The research methodology involved a comprehensive review of existing literature on fraud detection techniques, data preprocessing, feature selection, and model evaluation. The initial chapters of the thesis provide an introduction to the problem statement, objectives of the study, limitations, scope, significance, and the structure of the thesis. Additionally, key terms relevant to the study are defined to ensure clarity and understanding. The literature review chapter delves into ten key aspects related to predictive modeling for fraud detection, including machine learning algorithms, feature engineering techniques, anomaly detection methods, and data mining approaches. In the research methodology chapter, the process of developing the predictive modeling system is outlined, encompassing data collection, data preprocessing, feature selection, model training and evaluation, and performance metrics. The chapter also discusses the tools and technologies utilized in the study, such as Python programming language, scikit-learn library, and various machine learning algorithms. Chapter four presents a detailed discussion of the findings obtained from implementing the predictive modeling system for insurance claim fraud detection. The results highlight the effectiveness of different machine learning algorithms in accurately identifying fraudulent claims, as well as the impact of feature selection and data preprocessing techniques on model performance. The chapter also addresses challenges encountered during the research process and provides insights into potential areas for further improvement. Finally, the conclusion and summary chapter encapsulates the key findings, contributions, and implications of the study. The thesis concludes with a reflection on the significance of predictive modeling in combating insurance claim fraud, as well as recommendations for future research directions. Overall, this thesis contributes to the ongoing efforts to enhance fraud detection capabilities in the insurance industry through the application of advanced predictive modeling techniques.

Thesis Overview