Application of Machine Learning in Credit Risk Assessment for Banks

Thesis Abstract

Abstract
The banking sector plays a critical role in the economy by facilitating financial intermediation and allocation of resources. Central to the banking operations is the assessment and management of credit risk, which is vital for maintaining financial stability and sustainability. Traditional credit risk assessment methods have limitations in terms of accuracy and efficiency, leading to the need for innovative approaches. This thesis explores the application of machine learning techniques in credit risk assessment for banks, aiming to enhance the accuracy and effectiveness of the credit evaluation process. Chapter 1 provides an introduction to the research topic, presenting the background, problem statement, objectives, limitations, scope, significance, structure, and definition of terms. The chapter sets the foundation for the study, highlighting the importance of credit risk assessment in banking and the potential of machine learning to revolutionize the process. Chapter 2 presents a comprehensive literature review, covering ten key areas related to credit risk assessment, machine learning, and their intersection in the banking sector. The review synthesizes existing knowledge and identifies gaps in research, providing a theoretical framework for the study. Chapter 3 outlines the research methodology, detailing the research design, data collection methods, sampling techniques, variables, and analytical tools used in the study. The chapter discusses the rationale behind the chosen methodology and justifies its suitability for achieving the research objectives. Chapter 4 presents the findings of the study, analyzing the application of machine learning models in credit risk assessment for banks. The chapter discusses the performance of different machine learning algorithms in predicting credit risk and evaluates their effectiveness in comparison to traditional methods. The findings shed light on the potential benefits and challenges of implementing machine learning in banking practices. Chapter 5 provides a conclusion and summary of the thesis, highlighting the key findings, implications, and recommendations for future research and practical applications. The chapter discusses the contributions of the study to the field of credit risk assessment and outlines potential areas for further exploration and development. Overall, this thesis contributes to the growing body of knowledge on the application of machine learning in credit risk assessment for banks. By leveraging the power of artificial intelligence and data analytics, banks can enhance their risk management practices, improve decision-making processes, and ultimately strengthen their financial stability and performance in the dynamic and competitive banking industry.