Using Satellite Data in Financial Inclusion
Financial services providers (FSPs) that see an opportunity to reach financially excluded people in rural areas can use new technology to remotely gather and analyze data on potential customers.
High-quality satellite data are becoming increasingly available. By leveraging advances in machine learning—the ability of computers to analyze data quickly and at scale—providers can gain valuable insights into customers’ economic, environmental, and demographic characteristics. This guide explains foundational concepts of machine learning and how FSPs can apply those methods to leverage information contained in satellite images for the purpose of credit scoring.
The guide focuses on smallholder finance, but providers may find it useful for other applications as well, such as estimating local infrastructure, housing, and income levels; assessing the effectiveness of farming practices; crop insurance and risk calculations; and forecasting yields to combat food security problems.
|Author||Maria Fernandez Vidal & Peter Bull|
|Year of Publication||2019|
|Number of Pages||32 pages|
|Region / Country||Global /|
|Primary Language||English (en)|
|Keywords||Client Research and Product Development|