Project Abstract

Abstract
The application of machine learning in credit scoring for small and medium enterprises (SMEs) within the banking sector has emerged as a significant area of research due to its potential to enhance the efficiency and accuracy of credit assessment processes. This research study aims to investigate the effectiveness of machine learning algorithms in improving credit scoring for SMEs, with a focus on enhancing credit risk management practices and increasing financial inclusion for these enterprises. Chapter One provides an introduction to the research topic, discussing the background, problem statement, objectives, limitations, scope, significance, structure of the research, and definition of key terms. The increasing demand for credit facilities by SMEs, combined with the limitations of traditional credit scoring methods, highlights the need for innovative solutions such as machine learning algorithms to improve the assessment of creditworthiness. Chapter Two presents a comprehensive literature review on credit scoring methodologies, machine learning algorithms, and their applications in the banking sector. The review examines existing studies on credit scoring models for SMEs and explores the benefits and challenges associated with the adoption of machine learning techniques in credit assessment processes. Chapter Three outlines the research methodology, including the research design, data collection methods, sample selection criteria, variables, and data analysis techniques. The study employs a mixed-methods approach, combining quantitative analysis of credit scoring performance metrics with qualitative insights from banking experts and SME stakeholders. Chapter Four presents the findings of the research, analyzing the performance of machine learning algorithms in credit scoring for SMEs and comparing their accuracy and efficiency with traditional credit scoring models. The chapter discusses the key factors influencing credit risk assessment for SMEs and identifies opportunities for enhancing credit scoring practices through machine learning. Chapter Five concludes the research study by summarizing the key findings, discussing the implications for banking institutions and SMEs, and providing recommendations for future research and practical applications. The study contributes to the growing body of knowledge on the application of machine learning in credit scoring for SMEs and highlights the potential benefits of adopting advanced analytics tools in credit risk management processes. In conclusion, this research study underscores the importance of leveraging machine learning algorithms to enhance credit scoring practices for SMEs in the banking sector. By improving the accuracy and efficiency of credit assessment processes, banking institutions can better evaluate the creditworthiness of SMEs, mitigate risks, and support the growth and development of these enterprises.

Project Overview

The project topic "Application of Machine Learning in Credit Scoring for Small and Medium Enterprises in Banking Sector" focuses on the utilization of machine learning techniques to enhance credit scoring processes specifically tailored for small and medium enterprises (SMEs) within the banking sector. Credit scoring plays a critical role in evaluating the creditworthiness of individual and business borrowers, aiding financial institutions in making informed lending decisions. However, traditional credit scoring models often encounter challenges when assessing SMEs due to limited data availability, high risks, and complex financial structures. Machine learning offers a promising solution to address these challenges by leveraging advanced algorithms to analyze vast amounts of data and identify patterns that traditional methods may overlook. By applying machine learning algorithms to SME credit scoring, banks can improve accuracy, efficiency, and risk management in their lending practices. This research aims to explore the potential benefits, challenges, and implications of integrating machine learning techniques into credit scoring processes specifically designed for SMEs in the banking sector. The research will involve a comprehensive review of existing literature on credit scoring, machine learning applications in finance, and SME financing practices. By synthesizing theoretical frameworks and empirical studies, the study aims to establish a solid foundation for understanding the current landscape of credit scoring for SMEs and the potential of machine learning to enhance these practices. Furthermore, the research will delve into the methodologies and algorithms commonly used in machine learning for credit scoring, highlighting their strengths and limitations in the context of SME lending. Through empirical analysis and case studies, the project will demonstrate the practical implications of applying machine learning in credit scoring for SMEs. By evaluating the performance of machine learning models against traditional scoring methods, the research aims to provide insights into the effectiveness and feasibility of adopting these innovative approaches within banking institutions. Additionally, the study will address ethical considerations, regulatory compliance, and data privacy issues that may arise when implementing machine learning in credit scoring for SMEs. Overall, this research seeks to contribute to the growing body of knowledge on the intersection of machine learning and credit scoring within the banking sector, with a specific focus on SMEs. By shedding light on the opportunities and challenges associated with this integration, the study aims to inform policymakers, financial institutions, and stakeholders about the potential benefits of leveraging machine learning to enhance credit assessment processes for small and medium enterprises.