Adaptive Machine Learning Approaches for Analyzing SME Growth and Financial Challenges in Andhra Pradesh

Abstract

Role of SMEs in Andhra Pradesh: Small and Medium Enterprises (SMEs) have been key enablers for the development of Andhra Pradesh over the years, driving innovation, generating employment and leading to industrialization. This study explores the relationship between access to financing and SME growth through data science methods and computational approaches. By analyzing a dataset of 200 SMEs, we utilize advanced statistical and machine learning algorithms to uncover insights into financial health, borrowing behaviors, and credit access challenges. A few analytical methods (i.e., descriptive statistics, correlation analysis, and clustering) demonstrate heterogeneous characteristics of funding preference and performance metrics. The results reveal that SMEs display a preference for grants, as opposed to bank loans or self-funding, whereby exorbitant interest rates charged by banks and complicated cross-border regulations have deterred access to traditional funding avenues. Furthermore when doing predictive modeling the factors considered include external market factors such as trade volumes and currency fluctuations as well as internal factors relating to efficiency of operations on SME performance. Computational techniques discussed in the study could aid in creating targeted financial plans for SMEs, such as loan finding algorithms and decision support systems to enhance accessibility and sustainability. The advice in this regard would be to an institutional investor or asset owner to introduce digitalization platforms to support financing, to use AI-powered tools to improve levels of financial literacy, and to automatism regulatory compliance requirements. Policymakers and financial institutions, therefore, can build a strong ecosystem for SME growth, ensuring economic resilience and innovation in the region by adopting data-driven strategies.