Project Abstract

Abstract
In recent years, the banking industry has witnessed a significant shift towards the adoption of advanced technologies to enhance efficiency and accuracy in credit risk assessment. One such technology that has gained traction is machine learning, which offers the potential to revolutionize credit scoring processes in banking institutions. This research project aims to explore the application of machine learning in credit scoring for banking institutions, with a focus on its impact on efficiency, accuracy, and risk management. The study begins with an introduction to the background of credit scoring in banking institutions, highlighting the traditional methods and challenges faced in the process. A detailed examination of the problem statement underscores the need for innovative solutions to improve credit scoring accuracy and efficiency. The objectives of the study are outlined to guide the research towards evaluating the effectiveness of machine learning in credit scoring. The research methodology section presents a comprehensive overview of the methods employed in the study, including data collection, model development, and evaluation techniques. The use of machine learning algorithms such as logistic regression, decision trees, and neural networks is discussed in detail, emphasizing their relevance in credit scoring applications. The limitations and scope of the study are also addressed to provide a clear understanding of the research boundaries. A thorough literature review examines existing studies on machine learning in credit scoring, highlighting key findings and methodologies employed in previous research. The discussion of findings in chapter four presents the results of the study, including the performance comparison of machine learning models with traditional credit scoring methods. The implications of these findings for banking institutions are thoroughly analyzed, shedding light on the potential benefits and challenges associated with adopting machine learning in credit scoring processes. Finally, the conclusion and summary chapter provide a comprehensive overview of the research findings, emphasizing the significance of machine learning in enhancing credit scoring practices for banking institutions. The study concludes with recommendations for future research directions and practical implications for banking institutions looking to leverage machine learning for credit risk assessment. Overall, this research contributes to the growing body of knowledge on the application of machine learning in credit scoring for banking institutions, offering valuable insights into the potential benefits and challenges associated with this innovative technology. By embracing machine learning, banking institutions can enhance their credit risk assessment processes, leading to more informed lending decisions and improved risk management practices.

Project Overview