Artificial Intelligence in Sustainable Agricultural Lending: Enhancing Credit Access for Smallholder Farmers

Janak Suthar ()
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Janak Suthar: Aanand (IRMA)

Chapter 7 in Leveraging Emerging Technologies and Analytics for Empowering Humanity, Vol. 1, 2025, pp 141-156 from Springer

Abstract: Abstract The projected global population for 2050 is expected to reach 9.7 billion, necessitating a 70% increase in food production. However, achieving this goal presents significant challenges due to climate change, fluctuating temperatures, irregular rainfall patterns, reduced fertile land, and shrinking farmland. The agricultural sector requires substantial credit to improve its operations. In countries like India, where approximately two-thirds of the population relies on agriculture, it is important to note that 82% of farmers own less than one hectare of land, making it difficult for them to provide collateral for loans. Consequently, there needs to be greater clarity between the actual credit requirements of farmers and the financial assistance institutions offer. Additionally, interest rates for agricultural loans are typically much higher than those for other types of loans. Several factors contribute to this disparity, including unpredictability, the lack of credit history among farmers, difficulties in forecasting yields, and income instability, making the agricultural sector a high-risk investment. This article outlines a framework to address these challenges by leveraging the potential of artificial intelligence (AI). AI can bring more predictability to agriculture, reduce uncertainty, and, most importantly, enhance agricultural productivity through sustainable lending practices.

Keywords: Agri lending; Artificial intelligence; Machine learning (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:spr:prbchp:978-981-96-2548-2_7

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DOI: 10.1007/978-981-96-2548-2_7

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