MobileAid addresses the widespread development problems faced by many low- and middle-income country governments introducing cash-based crisis relief programs: (i) incomplete and low-quality databases, with serious gaps in key targeting information; and (ii) slow and costly targeting, enrollment, and payment models that can compound the secondary effects of a crisis. This is a common occurrence - while the digitization of cash continues to improve governments’ ability to distribute aid in times of crisis, identifying the poorest and most vulnerable individuals quickly and accurately remains a challenge. The target audience for this solution are global aid actors, including governments, INGOs, and NGOs, that operate in the social protection sector and can apply this model and/or our findings to improve the accuracy, inclusion, speed, and scale of their cash programs. MobileAid has so far been deployed at scale by the Governments of Togo and the DRC to deliver COVID-19 relief packages to impacted populations.

MobileAid is a data science-enabled model that identifies the poorest mobile phone subscribers in a country using machine learning and sends them cash transfers remotely, at unprecedented speed, and at a lower cost than available alternatives.