Project Abstract

Abstract
The banking sector plays a crucial role in facilitating economic activities by providing financial services to individuals and businesses. One of the key challenges faced by banks is the assessment of credit risk, which involves evaluating the likelihood of borrowers defaulting on their loans. Traditional credit risk assessment methods often rely on historical data and predefined rules, which may not be able to capture the complex and dynamic nature of credit risk. In recent years, machine learning algorithms have emerged as powerful tools for predictive analysis, offering the potential to improve the accuracy and efficiency of credit risk assessment. This research project aims to investigate the application of machine learning algorithms in predicting credit risk in retail banking. The study will focus on developing and implementing predictive models that can effectively evaluate the creditworthiness of borrowers. The research will be conducted using a dataset obtained from a retail banking institution, containing information on customer profiles, loan details, and historical repayment records. Chapter One provides an introduction to the research topic, including the background of the study, problem statement, objectives, limitations, scope, significance, structure of the research, and definition of key terms. Chapter Two presents a comprehensive literature review on credit risk assessment in banking, machine learning algorithms, and their applications in predictive analysis. Chapter Three outlines the research methodology, including data collection, data preprocessing, feature selection, model development, and model evaluation. The chapter also discusses the selection of machine learning algorithms, model training techniques, and performance metrics. In Chapter Four, the findings of the research are presented and discussed in detail. The chapter includes an analysis of the predictive models developed, their accuracy, reliability, and practical implications for credit risk assessment in retail banking. The chapter also explores the strengths and limitations of the machine learning algorithms used in the study. Chapter Five concludes the research project by summarizing the key findings, implications, and contributions to the field of credit risk assessment in retail banking. The chapter also discusses the potential for future research and the practical implications of using machine learning algorithms for credit risk prediction. Overall, this research project aims to contribute to the advancement of credit risk assessment practices in retail banking by exploring the capabilities of machine learning algorithms for predictive analysis. The findings of the study are expected to provide valuable insights for banking institutions seeking to enhance their credit risk management strategies and improve decision-making processes related to lending activities.

Project Overview