Application of Machine Learning in Credit Risk Assessment for Small Businesses in Banking Sector

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.