This paper examines the approaches used in the credit risk assessment of Small and Medium Enterprises (SMEs), focusing on both traditional and modern methods. The study explores how conventional techniques such as ratio analysis, financial statement evaluation, and credit history assessment compare with technology-driven methods including data analytics, artificial intelligence, machine learning, and alternative data usage. The research is based on quantitative and comparative analysis of secondary data collected from selected commercial banks and financial institutions. It aims to identify the effectiveness of these methods in assessing the creditworthiness of SMEs by analyzing key factors such as accuracy, efficiency, and reliability. The findings suggest that while traditional methods provide a strong foundational understanding of financial health, modern techniques enhance decision-making speed, predictive accuracy, and risk management capabilities. However, the effectiveness of each approach varies depending on data availability, technological adoption, and the nature of SMEs being evaluated.
A. et al. (Thu,) studied this question.
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