Project Abstract

Abstract
The evolution of technology has significantly influenced the banking sector, prompting the adoption of innovative solutions to enhance efficiency and accuracy in credit risk assessment processes. This research focuses on the application of Artificial Intelligence (AI) in credit risk assessment for Small and Medium Enterprises (SMEs) in the banking sector. The study aims to explore the potential benefits of AI in improving credit risk assessment for SMEs and address the challenges associated with traditional credit scoring methods. Chapter 1 provides an introduction to the research, presenting the background of the study, problem statement, objectives, limitations, scope, significance, structure of the research, and definition of key terms. The chapter sets the foundation for the study by highlighting the importance of AI in credit risk assessment for SMEs in the banking sector. Chapter 2 delves into a comprehensive literature review that examines existing studies, theories, and models related to credit risk assessment, AI applications in banking, and specifically, the use of AI in credit risk assessment for SMEs. The literature review explores key concepts such as machine learning algorithms, predictive analytics, and the advantages of AI-based credit scoring models. Chapter 3 outlines the research methodology, detailing the research design, data collection methods, sampling techniques, variables, and data analysis procedures. The chapter also discusses the ethical considerations and limitations of the research methodology to ensure the validity and reliability of the study findings. Chapter 4 presents a detailed discussion of the research findings, analyzing the impact of AI on credit risk assessment for SMEs in the banking sector. The chapter examines the effectiveness of AI algorithms in predicting creditworthiness, reducing risks, and improving decision-making processes for lending institutions. Chapter 5 concludes the research by summarizing the key findings, highlighting the implications of AI adoption in credit risk assessment for SMEs, and suggesting recommendations for future research and practical applications. The conclusion emphasizes the significance of AI in transforming credit risk assessment practices and enhancing financial inclusion for SMEs in the banking sector. In conclusion, this research contributes to the existing body of knowledge by exploring the potential of AI in credit risk assessment for SMEs in the banking sector. By leveraging advanced technologies such as AI, banks can enhance their risk management practices, streamline lending processes, and support the growth of SMEs. The findings of this study provide valuable insights for policymakers, banking institutions, and researchers seeking to harness the power of AI for more accurate and efficient credit risk assessment in the financial industry.

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