Mitigating Agricultural Lending Risk: An Advanced Analytical Approach

Aditi Singh () and Nishtha Jain
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Aditi Singh: Data Modeler, Experian Credit Information Company of India
Nishtha Jain: Data Modeler, Experian Credit Information Company of India

A chapter in Applied Advanced Analytics, 2021, pp 87-102 from Springer

Abstract: Abstract As per the Situation Assessment Survey (SAS) for Agricultural Households by NSSO 70th round, in 2012–13, almost 40% of the agricultural households still relied on non-institutional sources for their credit needs, an increase of almost 11% over 1990–91. Moneylenders still form a major part, around 26%, of that non-institutional credit. Even with the rising credit disbursements and loan waivers, we have not been able to improve the situation of our farmers. In FY 2018, banks disbursed only an additional 6.37% to this sector which is the lowest in a decade. Lack of sufficient information about the agricultural finance landscape and mounting NPAs have contributed to banks being reluctant to lend in this sector. In this study, we aim to build a credit risk assessment model for farmers to bridge the gap between them and the formal credit sector. We obtained a robust model that uses NSS data features and gives a better prediction of a farmer going to default than the one built on features currently captured by banks. These additional variables give necessary insights into farmer characteristics that could (1) help banks identify low-risk farmers and expand their lending and (2) help the government identify problem areas where their intervention is necessary to uplift farmers and bring them under the purview of formal credit.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:spr:prbchp:978-981-33-6656-5_8

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DOI: 10.1007/978-981-33-6656-5_8

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