Project Abstract

Abstract
The rapidly evolving landscape of the banking sector has necessitated the adoption of advanced technologies to enhance efficiency and accuracy in credit scoring processes, especially for small businesses. This research project focuses on the application of machine learning techniques in credit scoring to address the unique challenges faced by small businesses in accessing financial services. The primary objective of this study is to investigate the effectiveness of machine learning algorithms in improving credit scoring accuracy and risk assessment for small businesses in the banking sector. Chapter 1 provides a comprehensive 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 2 presents a detailed literature review that examines existing studies, theories, and methodologies related to credit scoring, machine learning, and small business finance. This chapter explores the current trends, challenges, and opportunities in credit scoring for small businesses, as well as the potential benefits of applying machine learning techniques in this context. In Chapter 3, the research methodology is outlined, detailing the research design, data collection methods, sampling techniques, and data analysis procedures. This chapter also discusses the selection and implementation of machine learning algorithms for credit scoring, as well as the evaluation metrics used to assess the performance of these algorithms. Chapter 4 presents a comprehensive discussion of the research findings, including the outcomes of the machine learning models applied to credit scoring for small businesses. The findings are analyzed and interpreted to provide insights into the effectiveness and implications of using machine learning in this context. Finally, Chapter 5 offers a conclusive summary of the research project, highlighting the key findings, implications, and recommendations for future research and industry practice. The study contributes to the growing body of knowledge on the application of machine learning in credit scoring for small businesses and provides valuable insights for banks, financial institutions, policymakers, and researchers interested in advancing financial inclusion and risk management strategies.

Project Overview