Thesis Abstract

Abstract
The financial industry has long relied on effective risk assessment techniques to evaluate the creditworthiness of individuals and businesses seeking loans. In recent years, the emergence of machine learning technologies has provided new opportunities to enhance the accuracy and efficiency of credit risk assessment models. This thesis explores the application of predictive modeling using machine learning techniques for credit risk assessment, with a focus on improving the predictive power of traditional credit scoring methods. Chapter 1 provides an introduction to the research topic, presenting the background of the study, the problem statement, research objectives, limitations, scope, significance, and the structure of the thesis. The chapter also includes the definition of key terms relevant to the study. Chapter 2 offers a comprehensive literature review, covering ten key areas relevant to credit risk assessment, machine learning in finance, predictive modeling techniques, and the integration of machine learning in credit scoring models. Chapter 3 outlines the research methodology employed in this study, detailing the data collection process, data preprocessing techniques, feature selection methods, model selection criteria, evaluation metrics, and validation techniques used to train and test the predictive models. Chapter 4 presents a detailed discussion of the findings obtained from applying various machine learning algorithms to credit risk assessment. The chapter highlights the performance of different models in terms of accuracy, precision, recall, and area under the receiver operating characteristic curve (AUC-ROC). Finally, Chapter 5 concludes the thesis by summarizing the key findings, discussing the implications of the research outcomes for the financial industry, and offering recommendations for future research in this area. The conclusion underscores the potential of machine learning techniques to revolutionize credit risk assessment practices, providing more accurate predictions and reducing the likelihood of default. Overall, this thesis contributes to the growing body of research on the application of machine learning in financial risk assessment, offering insights into the potential benefits and challenges of predictive modeling for credit risk assessment.

Thesis Overview