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FinTech Operational Architectures in Indian Agritech: A Conceptual Framework for Resolving Information Asymmetry, Transaction Costs, and Credit Rationing in Agricultural Value Chains
DOI:DOI:18.A003.aarf.J14I01.010768
Amit Ravindra Kale¹, Dr. U.S. Kollimath², Dr. Satish Kumar Lakshkar³
Abstract:
Background. Indian agriculture supports 46% of the workforce yet contributes only 18% of gross value added (Ministry of Finance, 2024), with non-institutional credit still accounting for roughly 30% of rural household debt despite three decades of policy intervention (NABARD, 2023). Against this backdrop, Indian Agri-FinTech ventures attracted cumulative funding exceeding USD 2.4 billion between 2017 and 2023 (Inc42, 2023).\r\nObjective. Existing literature documents that FinTech improves rural financial inclusion, but the operational architectures through which Agri-FinTech firms convert digital capability into corrected market failure remain under-theorised. This paper develops a conceptual framework explaining how FinTech operational models resolve three persistent failures in the Indian agricultural value chain: information asymmetry, transaction costs, and credit rationing.\r\nMethodology. A theory-building design combines a PRISMA-adapted systematic literature review (Page et al., 2021) with a purposive multiple-case analysis of nine Indian Agri-FinTech ventures (Eisenhardt, 1989; Yin, 2018): Arya.ag, DeHaat, Ninjacart, Jai Kisan, Samunnati, Growpital, Finsyst Innovations, CropIn, and Kissht. Data are triangulated across regulatory disclosures, RBI and NABARD reports, sectoral publications, corporate filings, and peer-reviewed scholarship.\r\nKey Findings. Four operational archetypes emerge: alternative-data credit scoring, embedded B2B supply-chain financing, warehouse-receipt-backed post-harvest lending, and parametric or embedded crop insurance. Each archetype maps to a distinct combination of theoretical mechanisms drawn from Transaction Cost Economics (Williamson, 1985), Information Asymmetry Theory (Akerlof, 1970; Stiglitz & Weiss, 1981), and the Resource-Based View (Barney, 1991). An integrative matrix links each archetype to specific value-chain nodes, with boundary conditions surfaced through counter-evidence including the 2023–2024 valuation correction.\r\n
