Farmer-level data collected from digital channels can be used to evaluate and rank potential borrowers in order of their likelihood to repay a loan. Such credit scoring models could help increase the scale and scope of lending to small holder farmers and augment existing value chain finance programs and informal lending.
Several concurrent industry trends are increasing the volume of digital data relevant to small holder farmers—from records of small holder farmer purchase and sales, the movement of goods through value chains (or ‘traceability’), and remote sensing of weather conditions, to farmers’ personal use of digital services on mobile phones and devices. Lenders and on-lenders (those in the value chain who borrow from the bank and in turn lend to small holder farmers) with access to such data may be able to develop credit scoring models to reasonably assess farmer credit risk at scale. This would allow lending to reach many more farmers than at present, while attempting to keep the risks and transaction costs low.
The value of each data set to a credit scoring model is a function of its availability from all farmers, relevance to farmer creditworthiness, cost to obtain, and reliability in predicting farmer credit risk. Ideally, a balanced scoring model would contain elements of credit history, transaction records, agronomic survey data and lifestyle-related demographics (marital status, household size, years in address, etc.), and could be augmented (or supplemented) by alternative data where feasible. This paper explores best practices and makes recommendations about introducing credit to farmers.
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