Application of Machine Learning in Credit Risk Assessment for Small and Medium Enterprises in Banking Sector

Thesis Abstract

The abstract for this thesis is as follows Title Application of Machine Learning in Credit Risk Assessment for Small and Medium Enterprises in Banking Sector Abstract
This thesis focuses on the application of machine learning techniques in credit risk assessment for small and medium enterprises (SMEs) within the banking sector. The objective of this study is to investigate how machine learning algorithms can enhance the accuracy and efficiency of credit risk assessment processes, particularly for SMEs that often face challenges in accessing credit due to limited financial history and resources. The research begins with a comprehensive literature review that explores the current methodologies and challenges in credit risk assessment for SMEs. It delves into the existing machine learning algorithms and their potential applications in credit risk assessment, highlighting the advantages and limitations of these methods. The review also examines previous studies and implementations of machine learning in credit risk assessment to provide a solid foundation for the research. In the methodology section, the research design and data collection approach are outlined. The study utilizes a combination of quantitative and qualitative methods to gather and analyze data from various sources, including financial statements, historical credit data, and machine learning models. The research methodology also includes the selection and implementation of specific machine learning algorithms tailored to the credit risk assessment needs of SMEs. The findings of the study are discussed in detail in the fourth chapter, presenting the results of applying machine learning algorithms to credit risk assessment for SMEs. The discussion covers the performance of different machine learning models, the accuracy of credit risk predictions, and the overall efficiency of the assessment process. The findings are analyzed in relation to the existing literature and practical implications for the banking sector. Finally, the thesis concludes with a summary of the key findings, implications, and recommendations for future research and practical applications. The study highlights the potential of machine learning techniques to revolutionize credit risk assessment for SMEs in the banking sector, offering improved accuracy, efficiency, and risk management capabilities. The research contributes valuable insights to the field of banking and finance, shedding light on the opportunities and challenges of integrating machine learning in credit risk assessment processes for SMEs.