Thesis Abstract

Abstract
Fraudulent activities in insurance claims have become a significant concern for insurance companies, leading to substantial financial losses and eroding trust in the industry. To address this challenge, this research project focuses on utilizing machine learning algorithms for fraud detection in insurance claims. The objective is to develop a robust and accurate fraud detection system that can effectively identify suspicious claims and mitigate fraudulent behavior. The study begins with a comprehensive review of the existing literature on fraud detection in insurance claims. This review explores the various machine learning algorithms and techniques that have been applied in this context, highlighting their strengths, limitations, and potential for improvement. By synthesizing the findings from previous research, this study aims to identify the most effective approaches for fraud detection in insurance claims. In the research methodology chapter, the study details the data collection process, including the sources of the insurance claims data used for training and testing the machine learning models. The methodology also outlines the steps involved in preprocessing the data, feature selection, model training, and performance evaluation. By following a systematic and rigorous methodology, the study ensures the credibility and reliability of the results obtained. The findings chapter presents the results of the experiments conducted to evaluate the performance of different machine learning algorithms for fraud detection in insurance claims. The study compares the accuracy, precision, recall, and F1 score of various models, highlighting their strengths and weaknesses in detecting fraudulent claims. Through this analysis, the study identifies the most effective algorithms for fraud detection and proposes recommendations for improving the detection performance further. In the conclusion and summary chapter, the study summarizes the key findings, discusses the implications of the results, and offers recommendations for future research in this area. The study concludes that machine learning algorithms can significantly enhance fraud detection capabilities in insurance claims and recommends their adoption by insurance companies to combat fraudulent activities effectively. Overall, this research project contributes to the ongoing efforts to combat insurance fraud by leveraging the power of machine learning algorithms. By developing a robust fraud detection system, insurance companies can protect their financial interests, maintain the trust of policyholders, and uphold the integrity of the insurance industry.

Thesis Overview