Richard is a smallholder maize farmer in Western Kenya. He used to borrow money from his family or friends to buy essential inputs to grow maize—seed, fertilizer, and equipment—because he couldn’t access a formal loan from a bank. Most banks consider him a high risk client because of his low-income and lack of credit history. Recently though, Richard obtained a $50 loan for his farming business. By processing and analyzing satellite imagery on his farmland and cropping cycle, in combination with other alternative data, a lending organization was able to assess his creditworthiness.
Two agriculture FinTechs integrated satellite imagery into their business models and product offerings, illustrating the promise and limitations of big data in financial services.
Today we are pleased to make the inaugural version of the Learning Advances in Digital Finance report available on the website. This document focuses on a series of “learning advances” identified during the first year of the Mastercard Foundation Partnership for Finance in a Digital Africa. Read now Learning Advances in Digital Finance 2017 The