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Default Risks Prediction

The challenge

In Tanzania, most smallholder farmers rely on sporadic rainfall to water their fields and most fishers rely  on kerosene lamps for light on their boats. With high-quality equipment, these farmers and fishers could  increase their productivity by up to 300 percent. But high upfront costs and a lack of rural distributors  keep these solutions out of reach for more than 40 million Tanzanians.

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The Impact 

61% of Simusolar’s customers live on less than $3.10 per day. With Simusolar’s equipment, customers  have the potential to double their incomes within a year. The company has served 2,000 farmers to date,  with plans to reach 10,000 customers by 2021. 

The Project 

Currently their largest issues are related to default risks of their customers. In the past they have collected  quite a lot of demographic data about their customers and have judged how likely they are to default with  the naked eye. This has become quite tedious and they have asked us to asses the default risk using  modelling techniques. For this we already have quite a big data dump consisting of various demographic  data such as how far from a river a farmer lives, how many kids he has, age, other professions, how much  land they have, etc. that can be used as independent variables. Furthermore we have a large amount of  payment data that we can use as independent variables. 

Aside from credit risks they would like to ensure the pumps will remain intact as long as possible. The  pumps have a few IoT sensors on them with which they would like to perform preventative maintenance.  This means that customers that use their pump is such a way that it will not last very long could be  contacted and told how to improve the longevity of their pumps.