Thesis Abstract

Abstract
This thesis explores the application of machine learning techniques in credit risk assessment for small businesses within the banking sector. The research aims to address the challenges faced by financial institutions in evaluating the creditworthiness of small businesses efficiently and accurately. The study focuses on leveraging machine learning algorithms to enhance the credit risk assessment process, thereby improving lending decisions and reducing potential risks associated with small business loans. Chapter 1 provides an introduction to the research topic, highlighting the background, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definitions of key terms. The literature review in Chapter 2 examines ten key studies on credit risk assessment, machine learning applications in banking, and specific methodologies relevant to small business lending. This chapter serves to establish a foundation for the subsequent research work. Chapter 3 outlines the research methodology, including the research design, data collection methods, sampling techniques, variables selection, model development, and evaluation criteria. The chapter also discusses ethical considerations and potential limitations in the research process. Chapter 4 presents a detailed discussion of the findings derived from the application of machine learning models to credit risk assessment for small businesses. The analysis includes performance metrics, model comparisons, and insights into the effectiveness of machine learning algorithms in predicting credit risk. In conclusion, Chapter 5 summarizes the key findings of the study and provides insights into the implications for the banking sector. The research contributes to the existing literature by demonstrating the potential of machine learning in enhancing credit risk assessment for small businesses. The findings suggest that machine learning algorithms can improve decision-making processes, reduce credit risks, and optimize lending practices in the banking sector. Overall, this thesis offers valuable insights into the practical applications of machine learning in credit risk assessment for small businesses, highlighting the benefits and challenges associated with adopting advanced analytics in the banking industry. The research outcomes have implications for financial institutions, policymakers, and researchers seeking to enhance credit risk assessment practices and support the growth of small businesses through improved access to financing.

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 advanced machine learning techniques in enhancing the credit risk assessment process for small businesses within the banking sector. In recent years, the banking industry has witnessed a surge in the adoption of machine learning technologies to streamline various operations, with credit risk assessment being a critical area of focus. Small businesses often face challenges in accessing credit facilities due to the perceived higher risk associated with lending to them. Traditional credit risk assessment methods may not adequately capture the unique characteristics and dynamics of small businesses, leading to suboptimal lending decisions. This research seeks to address this gap by leveraging machine learning algorithms to develop a more accurate and robust credit risk assessment framework tailored specifically for small businesses. By harnessing the power of big data analytics and machine learning models, the study aims to improve the predictive accuracy of credit risk assessment while also enhancing the efficiency and scalability of the process. Key components of the research will include a comprehensive review of existing literature on credit risk assessment, machine learning applications in banking, and specifically for small businesses. The methodology will involve data collection from financial institutions, small businesses, and relevant stakeholders, followed by the implementation of machine learning algorithms for credit risk assessment. The findings will be analyzed and discussed to evaluate the effectiveness of the proposed approach in improving credit risk assessment outcomes for small businesses. Overall, this project seeks to contribute to the advancement of credit risk assessment practices in the banking sector, particularly for small businesses, by harnessing the potential of machine learning technologies. The research outcomes are expected to provide valuable insights for financial institutions, policymakers, and other stakeholders involved in lending to small businesses, ultimately contributing to the promotion of financial inclusion and economic development.