Predictive modeling for insurance claim fraud detection using machine learning techniques

Thesis Abstract

Abstract
The insurance industry is constantly facing challenges in detecting and preventing fraudulent activities related to insurance claims. Traditional methods of fraud detection have proven to be insufficient in keeping up with the evolving tactics of fraudsters. This thesis aims to explore the application of predictive modeling and machine learning techniques in enhancing the detection of insurance claim fraud. The research focuses on developing a predictive model that can effectively identify and flag potentially fraudulent insurance claims, thereby assisting insurance companies in mitigating financial losses and maintaining the integrity of their operations. Chapter One provides an introduction to the research topic, highlighting the background of the study, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definition of terms. The chapter sets the foundation for the research by presenting a comprehensive overview of the importance of fraud detection in the insurance sector and the potential benefits of utilizing machine learning techniques for this purpose. Chapter Two consists of a thorough literature review that explores existing research and studies related to insurance claim fraud detection, predictive modeling, and machine learning applications in the insurance industry. The chapter aims to provide a deep understanding of the current state of the field and identify gaps that can be addressed through the proposed research. Chapter Three outlines the research methodology adopted in this study, including data collection methods, model development techniques, evaluation metrics, and validation procedures. The chapter details the steps taken to build and train the predictive model for insurance claim fraud detection, ensuring transparency and reproducibility in the research process. Chapter Four presents a detailed discussion of the findings obtained through the application of predictive modeling and machine learning techniques in detecting insurance claim fraud. The chapter analyzes the performance of the developed model, identifies key insights, and discusses the implications of the results for the insurance industry. Chapter Five serves as the conclusion and summary of the thesis, providing a comprehensive overview of the research findings, implications, limitations, and recommendations for future research in the field of insurance claim fraud detection using predictive modeling and machine learning techniques. The chapter concludes by emphasizing the significance of the research and its potential impact on improving fraud detection practices in the insurance sector. Overall, this thesis contributes to the growing body of knowledge on the application of advanced analytical techniques for enhancing fraud detection in the insurance industry. By developing a predictive model for insurance claim fraud detection, this research offers valuable insights and practical implications for insurance companies seeking to safeguard their operations and protect against fraudulent activities.