Project Abstract

Abstract
The use of machine learning in credit scoring has gained significant attention in the banking and finance sector due to its potential to enhance the accuracy and efficiency of credit risk assessment processes. This research project aims to explore the application of machine learning techniques in credit scoring for banks, with a focus on improving the credit evaluation process and reducing the risk of default. Chapter One provides an introduction to the research topic, presenting the background of the study, problem statement, objectives, limitations, scope, significance, structure of the research, and definition of key terms. Chapter Two consists of a comprehensive literature review that discusses ten key aspects related to machine learning in credit scoring, including existing models, algorithms, datasets, and challenges in implementation. Chapter Three outlines the research methodology, detailing the research design, data collection methods, sampling techniques, variables, model development process, and evaluation criteria. The chapter also covers ethical considerations and limitations of the methodology. In Chapter Four, the findings of the research are discussed in detail, highlighting the effectiveness of machine learning algorithms in credit scoring, the impact on credit risk assessment accuracy, and the potential benefits for banks in terms of decision-making and risk management. The chapter also addresses any limitations or challenges encountered during the research process. Finally, Chapter Five presents the conclusion and summary of the research project, summarizing the key findings, implications for the banking sector, and recommendations for future research. The study concludes that the application of machine learning in credit scoring has the potential to revolutionize the credit assessment process for banks, leading to more informed decisions, reduced risk exposure, and improved financial stability. Overall, this research project contributes to the growing body of knowledge on the application of machine learning in credit scoring for banks, offering insights into the benefits, challenges, and implications of adopting advanced technologies in the financial industry.

Project Overview