Thesis Abstract

Abstract
The financial industry has seen a significant transformation in recent years with the advent of machine learning technologies. One such area where machine learning has shown great potential is in credit scoring for banks. This thesis explores the application of machine learning algorithms in credit scoring to enhance the accuracy and efficiency of credit risk assessment processes in banks. The study aims to investigate how machine learning techniques can be leveraged to improve credit scoring models and mitigate risks associated with lending decisions. Chapter One provides an introduction to the research topic, including the background of the study, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definition of key terms. Chapter Two presents a comprehensive literature review on credit scoring, machine learning algorithms, and their applications in the financial sector. The chapter critically examines existing studies and identifies gaps in the literature that this research seeks to address. Chapter Three outlines the research methodology employed in this study, including data collection methods, variables selection, model development, and evaluation criteria. The chapter also discusses the dataset used for analysis and justifies the chosen machine learning algorithms for credit scoring. Furthermore, the ethical considerations and potential limitations of the research methodology are discussed. Chapter Four presents an in-depth discussion of the findings obtained from applying machine learning algorithms to credit scoring in banks. The chapter analyzes the performance of different machine learning models in predicting credit risk and compares their accuracy with traditional credit scoring methods. The implications of the findings for banks and recommendations for the implementation of machine learning in credit risk assessment are also discussed. Finally, Chapter Five provides a summary of the research findings, conclusions drawn from the study, and recommendations for future research in the field of credit scoring using machine learning. The thesis concludes with insights into the potential benefits of adopting machine learning in credit scoring for banks, such as improved risk management, enhanced decision-making processes, and increased efficiency in lending operations. In conclusion, this thesis contributes to the growing body of research on the application of machine learning in credit scoring for banks. The findings of this study have practical implications for banks looking to enhance their credit risk assessment processes and make more informed lending decisions. By leveraging machine learning technologies, banks can improve the accuracy and efficiency of credit scoring models, ultimately leading to better risk management practices and increased profitability.

Thesis Overview