Application of Machine Learning in Credit Scoring for Banks

Thesis Abstract

Abstract
The use of machine learning in credit scoring has gained significant attention in the banking sector due to its potential to enhance decision-making processes and improve credit risk assessment. This thesis explores the application of machine learning techniques in credit scoring for banks, with a focus on improving accuracy, efficiency, and predictive capabilities. The study aims to address the limitations of traditional credit scoring models by leveraging advanced machine learning algorithms to analyze and predict creditworthiness. Chapter 1 provides an introduction to the research topic, detailing the background of study, 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 applying machine learning in credit scoring for banks. Chapter 2 presents a comprehensive literature review that examines existing studies, methodologies, and applications of machine learning in credit scoring. The review covers ten key areas, including traditional credit scoring models, machine learning algorithms, data preprocessing techniques, feature selection methods, model evaluation metrics, and regulatory considerations. Chapter 3 outlines the research methodology employed in this study, detailing the research design, data collection methods, sample selection criteria, variables, data preprocessing steps, machine learning algorithms used, model evaluation techniques, and ethical considerations. The chapter provides a transparent overview of the research process and methodology adopted to achieve the study objectives. Chapter 4 presents a detailed discussion of the findings obtained from applying machine learning techniques in credit scoring for banks. The chapter analyzes the performance of different machine learning algorithms in predicting credit risk, identifies key factors influencing credit scoring outcomes, and discusses the implications of using machine learning for credit assessment. Chapter 5 concludes the thesis by summarizing the key findings, implications, and contributions of the study. The chapter highlights the significance of applying machine learning in credit scoring for banks, discusses the limitations and future research directions, and provides recommendations for enhancing credit risk assessment practices in the banking sector. In conclusion, this thesis contributes to the growing body of knowledge on the application of machine learning in credit scoring for banks. By leveraging advanced algorithms and techniques, banks can improve credit risk assessment, enhance decision-making processes, and ultimately optimize lending practices. The findings of this study provide valuable insights for practitioners, researchers, and policymakers seeking to enhance credit scoring methodologies in the financial industry.