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This review paper presents a conceptual framework for enhancing risk assessment models for small and medium-sized enterprises (SMEs) by integrating advanced analytics and machine learning techniques. The framework addresses the limitations of traditional risk models, which often fail to accurately assess the creditworthiness of SMEs due to their reliance on limited and outdated data. By incorporating diverse data sources and employing predictive modeling, the proposed framework offers a more comprehensive and dynamic approach to evaluating SME credit risk. This, in turn, facilitates greater financial inclusion by improving SMEs' access to capital, which is critical to economic growth and resilience in the United States. The paper also explores the implications for financial institutions and policymakers, emphasizing the need for regulatory support and ongoing research to maximize the benefits of these advanced risk assessment models.
Soremekun et al. (Fri,) studied this question.