Application of Machine Learning in Credit Scoring for Loan Approval in Banking

Thesis Abstract

Abstract
This thesis explores the application of machine learning techniques in credit scoring for loan approval within the banking sector. The traditional credit scoring process in banks typically relies on historical data and predefined rules to assess the creditworthiness of individuals applying for loans. However, with the advancements in machine learning algorithms and the availability of vast amounts of data, there is a growing interest in leveraging these technologies to enhance the accuracy and efficiency of credit scoring models. Chapter 1 provides an introduction to the study, setting the context for the research by discussing the background, problem statement, objectives, limitations, scope, significance, and structure of the thesis. The chapter also includes definitions of key terms to aid in understanding the subsequent chapters. Chapter 2 presents a comprehensive literature review, covering ten key areas related to credit scoring, machine learning, loan approval processes, and relevant studies in the field. This review aims to provide a theoretical foundation for the research and highlights the current state of the art in credit scoring using machine learning techniques. Chapter 3 details the research methodology employed in this study, including the research design, data collection methods, machine learning algorithms used, model evaluation techniques, and ethical considerations. The chapter also discusses the rationale behind the selection of specific methodologies and justifies their appropriateness for achieving the research objectives. Chapter 4 presents an in-depth discussion of the findings obtained from applying machine learning algorithms to credit scoring for loan approval. The chapter analyzes the performance of the models developed, compares them with traditional credit scoring methods, and discusses the implications of the results on the banking sector. Chapter 5 offers a conclusion and summary of the thesis, highlighting the key findings, contributions to the field, implications for practice, and areas for future research. The chapter also reflects on the limitations of the study and provides recommendations for improving credit scoring processes using machine learning in banking. Overall, this thesis contributes to the growing body of research on the application of machine learning in credit scoring for loan approval in banking. By exploring the potential benefits and challenges of incorporating machine learning techniques into credit assessment processes, this study aims to inform banks and policymakers on leveraging advanced technologies to enhance credit decision-making and mitigate risks in lending practices.