Application of Machine Learning in Predicting Insurance Claims Fraud

Thesis Abstract

Abstract
The insurance industry is constantly challenged by fraudulent activities, particularly in the realm of insurance claims. Fraudulent claims not only result in financial losses for insurance companies but also contribute to the overall increase in premiums for policyholders. The advent of machine learning techniques has provided a promising avenue for the detection and prevention of insurance claims fraud. This thesis investigates the application of machine learning in predicting insurance claims fraud, with a focus on improving fraud detection accuracy and efficiency. The research begins with a comprehensive literature review in Chapter Two, which examines existing studies and methodologies related to insurance claims fraud detection, machine learning algorithms, and predictive modeling techniques. The review highlights the strengths and limitations of current approaches and sets the foundation for the proposed research methodology. Chapter Three details the research methodology employed in this study, including data collection, preprocessing, feature selection, model training, and evaluation techniques. The chapter elaborates on the dataset used, the selection of relevant features, and the implementation of various machine learning algorithms such as logistic regression, decision trees, random forests, and neural networks. Chapter Four presents a detailed discussion of the findings obtained from the application of machine learning algorithms to predict insurance claims fraud. The chapter explores the performance metrics of each algorithm, such as accuracy, precision, recall, and F1 score, to evaluate the effectiveness of fraud detection. The results are analyzed and compared to identify the most suitable algorithm for predicting insurance claims fraud. In conclusion, Chapter Five summarizes the key findings of the study, discusses the implications of the research outcomes, and provides recommendations for further research in this area. The study contributes to the body of knowledge on insurance claims fraud detection by demonstrating the potential of machine learning techniques in enhancing fraud detection capabilities within the insurance industry. Overall, this thesis serves as a valuable resource for insurance companies, researchers, and policymakers seeking to leverage machine learning for the prediction and prevention of insurance claims fraud. The findings presented in this study offer insights into the practical applications of machine learning in combating fraudulent activities, ultimately leading to more secure and efficient insurance claim processes.