Access to digital financial services is fundamental to enabling those living in poverty around the world to become more economically stable, prosperous, and resilient. Using statistical and spatial analytical means, this project aims to use probabilistic models to predict the location of financial touch points that were previously not known.
Machine learning prediction approach
Based on authoritative and user-contribute data
Produced a mobile data collection platform
In cooperation with Spatial Development International