The Interplay of Experimentation and ML to Aid in Repayment of Micro-loans in Sub-Saharan Africa
The Interplay of Experimentation and ML to Aid in Repayment of Micro-loans in Sub-Saharan Africa


More than 600 million people in Sub-Saharan Africa have no access to electricity, and the majority of those have no documented financial history. These two facts set the stage for some incredibly cool applications of data science. A family can light their home and keep necessary electronics (such as a cell phone) charged using a small solar panel and battery, but most solar devices are not affordable to a vast number of people in this region, a large portion of which make $2 a day or less.

Widespread adoption of technology for digital financial transactions opens up energy access to those on the lower end of the income spectrum, with several organizations now offering 1-3 year financial loans on solar home systems. This brings the price of a kit to as low as 20 cents per day, which starts to rival the cost of existing energy sources (such as kerosene lanterns and wood fires, which are environmentally unfriendly and often dangerous.) However, a rural Ugandan farmer, for example, often has a very sporadic income with unpredictable financial shocks so paying off a loan over 1-3 years is not a trivial thing.

We use data from multiple different sources - customer demographics, regional information, IoT data from the solar kit itself, as well as the customer’s own past repayment patterns - to understand when and how to intervene with a customer falling behind on their loan, to help increase their chance of successfully paying it off. I’ll talk about the mix of experimentation and machine learning, as well as a little bit about issues that we’ve encountered while building for scale, in using data to increase financial inclusion and adoption of sustainable energy sources in this rapidly growing population.


Brianna leads the data science team at Fenix International. Their work spans multiple countries, including the US, Uganda, Zambia, and Ivory Coast. She and the data team at Fenix work on a wide range of problems to help provide clean, safe, and sustainable energy to people living off the grid in Sub-Saharan Africa. She has a bachelor's degree in Physics from Johns Hopkins University, a master's degree in Physics from the University of Wisconsin - Madison, and a Ph.D. in Neuroscience from the University of Wisconsin - Madison. After years of particle physics and functional MRI analyses, she took a break from academia and served as a Peace Corps volunteer in Northern Uganda. She's delighted to use her background in big data at the perfect crossroads of sustainable energy and energy access for underserved populations.

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