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.

Thesis Overview

The project titled "Development of a Machine Learning Model for Predicting Insurance Claim Fraud" aims to address the growing issue of insurance claim fraud within the insurance industry. Fraudulent insurance claims not only result in significant financial losses for insurance companies but also contribute to increased premiums for honest policyholders. By leveraging machine learning techniques, this project seeks to develop a predictive model that can accurately identify potentially fraudulent insurance claims at an early stage. The research will focus on utilizing historical insurance claim data to train and validate the machine learning model. Various features such as claim amount, claim type, policyholder information, and claim history will be analyzed to identify patterns and anomalies associated with fraudulent claims. The model will be designed to detect suspicious patterns, behaviors, or inconsistencies that are indicative of potential fraud. The project will also explore the use of different machine learning algorithms such as logistic regression, random forest, and neural networks to determine the most effective approach for predicting insurance claim fraud. By comparing the performance of these algorithms, the research aims to identify the most accurate and efficient model for fraud detection in the insurance industry. Furthermore, the project will emphasize the importance of interpretability and transparency in the machine learning model. It is crucial for insurance companies to understand how the model makes predictions in order to gain trust and acceptance from stakeholders. Therefore, the research will focus on developing an interpretable model that can provide insights into the factors influencing the prediction of fraudulent claims. Overall, the project "Development of a Machine Learning Model for Predicting Insurance Claim Fraud" is significant in its potential to help insurance companies mitigate the risks associated with fraudulent claims. By leveraging the power of machine learning, this research aims to enhance fraud detection capabilities, reduce financial losses, and ultimately create a more secure and trustworthy insurance environment for both insurers and policyholders.