Application of Machine Learning in Credit Risk Assessment for Commercial Banks

Thesis Abstract

Abstract
The ever-evolving landscape of the banking and finance sector has led to the adoption of innovative technologies to enhance efficiency and accuracy in risk assessment processes. This thesis explores the application of machine learning techniques in credit risk assessment for commercial banks. The study aims to investigate how machine learning algorithms can improve the accuracy of credit risk assessment models, leading to better decision-making processes within commercial banks. The research methodology involves a comprehensive review of existing literature on credit risk assessment, machine learning algorithms, and their applications in the banking sector. The study will also analyze real-world data from commercial banks to evaluate the performance of machine learning models in predicting credit risk. Chapter One provides an introduction to the research topic, background information, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definition of key terms. Chapter Two presents a detailed literature review on credit risk assessment models, traditional methods, machine learning algorithms, and their applications in the banking industry. Chapter Three outlines the research methodology, including data collection methods, data preprocessing techniques, feature selection, model development, model evaluation, and validation procedures. The chapter also discusses the ethical considerations and limitations of the study. Chapter Four presents the findings of the study, including the performance evaluation of different machine learning models in credit risk assessment. The chapter discusses the strengths and weaknesses of each model and provides insights into their practical applications in commercial banks. Chapter Five concludes the thesis by summarizing the key findings, highlighting the contributions to the field of credit risk assessment, and discussing the implications for commercial banks. The chapter also offers recommendations for future research directions and practical implications for industry professionals. Overall, this thesis contributes to the growing body of knowledge on the application of machine learning in credit risk assessment for commercial banks. The study provides valuable insights into how machine learning algorithms can enhance the accuracy and efficiency of credit risk assessment processes, ultimately leading to improved decision-making and risk management practices within the banking sector.