Role of Artificial Intelligence in Strengthening Self-Help Groups (SHGs) andMicrofinance Institutions in Rural India: A Systematic Review of Literature
Keywords:
Artificial Intelligence; Self-Help Groups (SHGs); Microfinance Institutions; Financial Inclusion; Rural India; Credit Scoring; Vernacular NLP; NABARD E-ShaktiAbstract
India's Self-Help Group (SHG) ecosystem is the largest microfinance model in the world, covering over 17.75 crore households as of March 2024 and representing 83.52% women-only groups. Despite this remarkable scale, SHGs and Microfinance Institutions (MFIs) in rural India continue to grapple with chronic inefficiencies: poor recordkeeping, high loan default rates, geographic exclusion, language barriers, and the persistent gap between savings-linked and credit-linked groups. Artificial Intelligence (AI) has emerged as a potentially transformative force in addressing these structural challenges, offering tools for automated credit scoring, fraud detection, voice-based customer engagement in vernacular languages, predictive loan management, and digital record digitization. This paper presents a comprehensive systematic review of over 40 peer-reviewed studies, institutional reports, and technical papers published between 2015 and 2025, examining the role and impact of AI technologies in strengthening SHGs and MFIs in rural India. The review traces the evolution of digital interventions from NABARD's E-Shakti digitization project launched in 2015 to the current generation of AI-powered loan recommendation systems, multilingual conversational AI, and generative AI tools for financial literacy. Key AI applications reviewed include machine learning-based credit risk assessment, Natural Language Processing (NLP) for vernacular communication, predictive analytics for default prevention, AI-powered fraud detection in digital payments, and AI-enabled Management Information Systems (MIS) for SHG operations. Critical research gaps are identified, including: the near absence of SHG-specific AI datasets, inadequate attention to caste and gender bias in AI credit models for rural women, the underdeveloped regulatory framework for AI in rural microfinance, insufficient evaluation of long-term social outcomes, and the lack of offline-capable AI tools for areas with poor connectivity. The paper concludes by proposing a forward- looking research agenda centered on inclusive, responsible AI deployment for India's rural financial sector.
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