Thesis Abstract

Abstract
The banking sector plays a crucial role in the financial ecosystem by providing essential services such as lending and risk assessment. Credit risk assessment, in particular, is a fundamental process in banking that involves evaluating the creditworthiness of borrowers to determine the likelihood of default on loan repayments. Traditional credit risk assessment methods have limitations in terms of accuracy and efficiency, leading to the exploration of alternative approaches such as machine learning algorithms. This thesis focuses on the utilization of machine learning algorithms for credit risk assessment in banking. The research aims to investigate the effectiveness of machine learning techniques in improving the accuracy and efficiency of credit risk assessment processes. The study will explore the application of various machine learning algorithms, such as decision trees, random forests, support vector machines, and neural networks, to analyze historical credit data and predict credit risk outcomes. Chapter 1 provides an introduction to the research topic, background information on credit risk assessment in banking, the problem statement, objectives of the study, limitations, scope, significance, structure of the thesis, and definitions of key terms. Chapter 2 presents a comprehensive literature review covering ten key areas related to credit risk assessment, machine learning algorithms, and their applications in the banking sector. Chapter 3 outlines the research methodology, including the research design, data collection methods, data preprocessing techniques, model development, model evaluation, and validation procedures. The chapter also discusses ethical considerations and potential limitations of the research methodology. Chapter 4 presents a detailed discussion of the research findings, including the performance evaluation of different machine learning algorithms in credit risk assessment tasks. The chapter explores the strengths and weaknesses of each algorithm and provides insights into their practical implications for the banking sector. In Chapter 5, the thesis concludes with a summary of the key findings, implications for practice, contributions to the existing literature, and recommendations for future research. The study highlights the potential of machine learning algorithms to enhance credit risk assessment processes in banking and emphasizes the importance of continuous innovation and adaptation in the financial industry. Overall, this thesis contributes to the growing body of research on the application of machine learning in banking and provides valuable insights into the potential benefits of adopting advanced analytics techniques for credit risk assessment. The findings of this study have implications for financial institutions seeking to improve their risk management practices and enhance decision-making processes in the lending domain.

Thesis Overview