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 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.