Predictive modeling for credit risk assessment in commercial banking using machine learning algorithms

Thesis Abstract

Abstract
This thesis explores the application of predictive modeling for credit risk assessment in commercial banking through the utilization of machine learning algorithms. The study aims to enhance the accuracy and efficiency of credit risk assessment processes in commercial banks by leveraging advanced computational techniques. The research methodology involves a comprehensive literature review on credit risk assessment, machine learning algorithms, and predictive modeling techniques. Chapter One provides an introduction to the research topic, including background information, problem statement, objectives, limitations, scope, significance of the study, structure of the thesis, and definition of key terms. Chapter Two presents a detailed literature review covering ten key aspects related to credit risk assessment, machine learning algorithms, and predictive modeling in commercial banking. Chapter Three outlines the research methodology, including the research design, 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 research methodology. Chapter Four presents an elaborate discussion of the findings obtained from applying machine learning algorithms to predict credit risk in commercial banking. The chapter includes the analysis of model performance, comparison of different algorithms, interpretation of results, and implications for credit risk management practices in commercial banks. Chapter Five concludes the thesis by summarizing the key findings, discussing the implications for commercial banking practices, highlighting the contributions to the field of credit risk assessment, and suggesting recommendations for future research. The study contributes to the advancement of credit risk assessment practices in commercial banking by demonstrating the effectiveness of predictive modeling techniques in improving decision-making processes and mitigating credit risk.