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.

Thesis Overview

The project titled "Application of Machine Learning in Credit Risk Assessment for Banks" aims to explore the potential benefits and challenges of integrating machine learning techniques in the credit risk assessment process within the banking sector. This research is motivated by the increasing complexity and volume of data available to financial institutions, as well as the need for more accurate and efficient risk evaluation methods. The research will begin with a comprehensive introduction, providing background information on credit risk assessment in banking and the traditional methods currently used. The problem statement will highlight the limitations and drawbacks of existing approaches, leading to the research objective to investigate how machine learning algorithms can enhance the accuracy and speed of credit risk assessment for banks. The study will also outline the scope and limitations of the research, defining the specific boundaries within which the investigation will be conducted. Additionally, the significance of the study will be emphasized, focusing on the potential impact of implementing machine learning in credit risk assessment on financial institutions, regulators, and customers. A detailed literature review will explore existing research and practical applications of machine learning in credit risk assessment, analyzing various algorithms, methodologies, and case studies. This section aims to provide a theoretical foundation for the research and identify gaps in current knowledge that the study will address. The research methodology section will outline the approach and methods used to collect and analyze data for the study. This will include details on the data sources, variables, sampling techniques, and the specific machine learning algorithms that will be employed in the credit risk assessment model. The discussion of findings chapter will present the results of the study, evaluating the performance of the machine learning model in predicting credit risk compared to traditional methods. The analysis will also consider the implications of the findings for banking practices and the potential challenges of implementing machine learning in credit risk assessment. Finally, the conclusion and summary chapter will summarize the key findings of the research, highlighting the contributions to the field of credit risk assessment and suggesting avenues for future research and practical implementation in the banking sector. Overall, this project aims to advance understanding of the application of machine learning in credit risk assessment and provide valuable insights for banks looking to enhance their risk management practices.