The integration of artificial intelligence (AI), machine learning and digital platforms into banking systems has transformed small-firm credit assessment, particularly in contexts characterised by information asymmetry and limited collateral. Small and Medium Enterprises (SMEs) in traditional industries remain among the most credit-constrained segments of the economy, despite their critical contribution to employment and regional development. This study empirically examines the role of AI-enabled digital credit in improving access to finance, financial stability and sustainable practices among SMEs in the Tussar (Vanya) silk industry of Jharkhand, India. Using primary field survey data from Tussar silk enterprises in Hazaribagh and Ranchi districts, supplemented by district-level credit statistics from the Reserve Bank of India and sectoral information from JHARCRAFT, the study employs logit and regression models to analyse credit approval, repayment stress and adoption of environmentally sustainable practices. The findings indicate that higher exposure to AI-enabled lending processes is associated with improved loan approval outcomes without a corresponding increase in repayment stress, while also facilitating the adoption of sustainable production practices. The paper contributes to the literature on digital finance by providing micro-level evidence from a traditional artisanal sector and offers policy insights on inclusive, stable and sustainable digital banking in emerging economies.
Ifsha Khurshid (Sun,) studied this question.
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