Thesis Abstract

Abstract
This thesis explores the application of machine learning techniques in credit risk assessment for small businesses within the banking sector. The primary objective of this research is to investigate how machine learning algorithms can enhance the accuracy and efficiency of assessing credit risk for small businesses, ultimately aiding banks in making informed lending decisions. The study delves into the background of credit risk assessment, highlighting the challenges faced by traditional methods and the potential benefits of integrating machine learning technologies. Chapter One provides an introduction to the research topic, offering insights into the background of the study, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definitions of key terms. Chapter Two presents a comprehensive literature review comprising ten key aspects related to credit risk assessment, machine learning in banking, small business lending, and existing studies on the topic. The review of literature aims to provide a solid foundation for the empirical investigation conducted in this thesis. Chapter Three outlines the research methodology employed in this study, including the research design, data collection methods, sampling techniques, variables, data analysis tools, and ethical considerations. The chapter also discusses the limitations of the methodology and strategies adopted to address potential biases. Chapter Four presents the detailed findings of the empirical analysis, examining the performance of various machine learning models in credit risk assessment for small businesses. The results are analyzed and discussed in relation to the research objectives, providing insights into the effectiveness of machine learning algorithms in enhancing credit risk assessment processes. Finally, Chapter Five offers a conclusion and summary of the thesis, highlighting the key findings, implications for practice, theoretical contributions, and recommendations for future research. The study concludes that machine learning technologies have the potential to revolutionize credit risk assessment in the banking sector, particularly for small businesses. By leveraging advanced analytics and predictive modeling, banks can improve their risk management practices, enhance decision-making processes, and better support the financial needs of small enterprises. This research contributes to the existing literature on credit risk assessment and machine learning applications in banking, opening new avenues for further exploration and innovation in the field.

Thesis Overview

The project titled "Application of Machine Learning in Credit Risk Assessment for Small Businesses in Banking Sector" aims to explore the utilization of machine learning techniques in enhancing credit risk assessment processes specifically tailored for small businesses within the banking sector. In recent years, the increasing importance of accurate credit risk assessment has become evident due to the dynamic nature of the financial industry and the need to mitigate potential risks associated with lending to small businesses. Small businesses play a crucial role in the economy by driving innovation, creating job opportunities, and contributing significantly to economic growth. However, these entities often face challenges when seeking financial support, particularly in obtaining credit from traditional financial institutions. One of the primary obstacles faced by small businesses is the stringent credit assessment criteria employed by banks, which may limit their access to much-needed funding. Machine learning, as a subset of artificial intelligence, offers a promising approach to improving credit risk assessment processes for small businesses. By leveraging advanced algorithms and predictive analytics, machine learning models can analyze vast amounts of data to identify patterns and trends that traditional methods may overlook. This project seeks to harness the power of machine learning to develop more accurate and efficient credit risk assessment models tailored to the unique characteristics of small businesses. The research will involve a comprehensive review of existing literature on credit risk assessment, machine learning applications in finance, and the specific challenges faced by small businesses in accessing credit. By synthesizing insights from these diverse domains, the project aims to identify best practices and innovative approaches that can be applied to enhance credit risk assessment for small businesses. Furthermore, the research methodology will involve data collection from relevant sources, such as financial institutions, regulatory bodies, and small business owners. The data will be used to train and validate machine learning models, allowing for the evaluation of their performance in predicting credit risk for small businesses accurately. The project will also explore the ethical considerations and potential implications of implementing machine learning algorithms in credit risk assessment processes. Through a detailed analysis of findings and discussions, the project aims to provide valuable insights into the benefits and challenges associated with the application of machine learning in credit risk assessment for small businesses. By highlighting the potential improvements in accuracy, efficiency, and inclusivity that machine learning can offer, the research seeks to contribute to the ongoing discourse on enhancing financial services for small businesses within the banking sector. In conclusion, the project "Application of Machine Learning in Credit Risk Assessment for Small Businesses in Banking Sector" aspires to bridge the gap between technological innovation and financial inclusion by proposing novel approaches to credit risk assessment that can empower small businesses and promote sustainable economic development.