Development of a Machine Learning Model for Predicting Insurance Claim Fraud

Thesis Abstract

Abstract
The insurance industry faces significant challenges in detecting and preventing fraudulent claims, which can lead to substantial financial losses and damage to reputation. In response to this pressing issue, this thesis presents a comprehensive study on the development of a machine learning model for predicting insurance claim fraud. The primary objective of this research is to leverage advanced machine learning algorithms to improve the accuracy and efficiency of fraud detection in the insurance sector. The thesis begins with an introduction that outlines the background of the study, identifies the problem statement, presents the research objectives, discusses the limitations and scope of the study, highlights the significance of the research, and provides an overview of the thesis structure. The literature review in Chapter Two critically examines existing research on fraud detection in insurance, focusing on key concepts, methodologies, and technologies employed in similar studies. This chapter aims to establish a solid theoretical foundation for the development of the proposed machine learning model. Chapter Three details the research methodology employed in this study, covering aspects such as data collection, preprocessing techniques, feature selection, model development, evaluation metrics, and validation methods. The methodology section provides a transparent and systematic approach to ensure the validity and reliability of the research findings. Chapter Four presents a comprehensive discussion of the findings obtained from implementing the machine learning model for predicting insurance claim fraud. This chapter analyzes the performance of the model, interprets key insights, discusses challenges encountered during the implementation, and proposes recommendations for future research in this field. In the final chapter, Chapter Five, the thesis concludes with a summary of the key findings, implications of the research, contributions to the field of insurance fraud detection, and recommendations for practitioners and policymakers. The conclusion also reflects on the limitations of the study and suggests avenues for further research to enhance the effectiveness of fraud detection mechanisms in the insurance industry. Overall, this thesis contributes to the evolving field of insurance fraud detection by offering a novel approach that integrates machine learning techniques with traditional fraud detection methods. By developing a robust predictive model for identifying fraudulent insurance claims, this research aims to empower insurance companies with the tools and insights needed to mitigate the risks associated with fraudulent activities, thereby safeguarding the financial interests of both insurers and policyholders.