Thesis Abstract

Abstract
The banking industry plays a crucial role in the global economy by facilitating financial transactions and providing essential services to individuals and businesses. In this context, the assessment of credit risk is a fundamental aspect of banking operations, as it directly impacts the financial health and stability of banks. Traditional credit risk assessment methods have been largely manual and rule-based, leading to inefficiencies and limitations in accurately predicting credit defaults. This research project focuses on leveraging machine learning techniques to enhance credit risk assessment in banks. Chapter 1 provides an introduction to the research topic, outlining the background, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definition of key terms. The chapter sets the foundation for understanding the importance of credit risk assessment in banking and the potential benefits of applying machine learning algorithms to improve accuracy and efficiency. Chapter 2 presents a comprehensive literature review that explores existing studies, frameworks, and models related to credit risk assessment and machine learning applications in the banking sector. The review covers key concepts such as credit scoring, risk management techniques, and the evolution of machine learning in credit risk assessment. Chapter 3 details the research methodology used in this study, including data collection, preprocessing, feature selection, model selection, and evaluation metrics. The chapter also discusses the dataset used for training and testing machine learning models, as well as the specific algorithms employed in the credit risk assessment process. Chapter 4 presents a detailed analysis and discussion of the findings obtained from implementing machine learning algorithms for credit risk assessment. The chapter evaluates the performance of different models in predicting credit defaults and compares them against traditional methods to demonstrate the effectiveness of machine learning in improving accuracy and efficiency. Chapter 5 concludes the thesis by summarizing the key findings, discussing the implications of the research, and providing recommendations for future studies and practical applications. The conclusion emphasizes the potential of machine learning in revolutionizing credit risk assessment practices in banks and highlights the importance of continuous innovation and adaptation in the ever-changing financial landscape. In conclusion, this research project contributes to the existing body of knowledge by showcasing the benefits of applying machine learning techniques in credit risk assessment for banks. By harnessing the power of data-driven algorithms, banks can enhance their risk management processes, make more informed lending decisions, and ultimately improve their financial stability and performance in the long run.

Thesis Overview