Application of Machine Learning in Credit Risk Assessment for Small and Medium Enterprises in Banking Sector

Thesis Abstract

Abstract
The banking sector plays a crucial role in the economy by providing financial services to individuals and businesses. One of the key functions of banks is to assess the creditworthiness of borrowers to manage credit risk effectively. In recent years, advancements in technology, particularly in machine learning, have provided new opportunities for banks to enhance their credit risk assessment processes. This thesis investigates the application of machine learning in credit risk assessment for small and medium enterprises (SMEs) in the banking sector. The study begins with a comprehensive introduction that sets the context for the research. It delves into the background of the study, highlighting the importance of credit risk assessment in banking and the challenges faced by banks in evaluating the creditworthiness of SMEs. The problem statement identifies the gaps in traditional credit risk assessment methods and emphasizes the need for innovative solutions. The objectives of the study are outlined to guide the research process, while the limitations and scope of the study are defined to provide clarity on the boundaries of the research. The significance of the study is discussed to underscore its potential contributions to the banking sector, and the structure of the thesis is presented to provide an overview of the chapters. Chapter Two presents a detailed literature review that explores existing research on credit risk assessment, machine learning techniques, and their applications in the banking sector. The review encompasses ten key themes, including the challenges of credit risk assessment for SMEs, the advantages of machine learning in credit risk modeling, and the ethical considerations of using machine learning algorithms in banking. Chapter Three focuses on the research methodology employed in the study. It covers various aspects such as the research design, data collection methods, sampling techniques, and the machine learning algorithms utilized for credit risk assessment. The chapter also discusses model evaluation techniques, data preprocessing steps, and the validation process to ensure the reliability and validity of the study findings. In Chapter Four, the findings of the research are presented and discussed in detail. The application of machine learning algorithms in credit risk assessment for SMEs is analyzed, highlighting the performance of the models and their ability to predict credit risk accurately. The chapter also examines the factors influencing credit risk for SMEs and provides insights into the key determinants of creditworthiness. Chapter Five serves as the conclusion and summary of the thesis, encapsulating the key findings, implications, and recommendations derived from the study. The conclusion reflects on the research objectives and discusses the practical implications of applying machine learning in credit risk assessment for SMEs in the banking sector. The summary provides a concise overview of the research journey and its contributions to the field of banking and finance. In conclusion, this thesis contributes to the existing literature by investigating the application of machine learning in credit risk assessment for SMEs in the banking sector. By leveraging advanced technology and data analytics, banks can enhance their risk management practices and support the growth of SMEs. The findings of this study have the potential to inform policy decisions, improve credit risk assessment processes, and foster financial inclusion for small businesses in the banking sector.