Thesis Abstract

Abstract
The insurance industry relies heavily on accurate predictions of claim frequency to manage financial risks effectively. Machine learning algorithms have emerged as powerful tools for analyzing complex data patterns and making predictions in various domains. This thesis explores the application of machine learning algorithms in predicting insurance claims frequency to enhance risk management practices in the insurance sector. Chapter 1 provides an introduction to the research topic, including the background of the study, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definition of key terms. Chapter 2 presents a comprehensive literature review covering ten key aspects related to machine learning, insurance claims frequency prediction, and relevant methodologies. Chapter 3 outlines the research methodology, detailing the data collection process, selection of machine learning algorithms, feature engineering techniques, model training and evaluation methods, and performance metrics used to assess the predictive accuracy of the models. The chapter also discusses the ethical considerations and challenges encountered during the research process. Chapter 4 presents a detailed discussion of the findings obtained through the application of machine learning algorithms in predicting insurance claims frequency. The chapter explores the performance of different algorithms, the impact of feature selection on model accuracy, and the insights gained from the predictive models. Additionally, the chapter examines the practical implications of the research findings for risk management practices in the insurance industry. Chapter 5 concludes the thesis by summarizing the key findings, discussing the implications of the research results, and highlighting potential areas for future research. The conclusion also emphasizes the significance of integrating machine learning techniques into insurance claim frequency prediction to improve decision-making processes and enhance risk management strategies in the insurance sector. Overall, this thesis contributes to the growing body of knowledge on the application of machine learning algorithms in insurance risk management and provides valuable insights for industry practitioners, researchers, and policymakers seeking to leverage advanced analytics for predicting insurance claims frequency and optimizing risk mitigation strategies.

Thesis Overview