  Access to financial institutions is difficult in developing economies and
especially for the poor. However, the widespread adoption of mobile phones has
enabled the development of mobile money systems that deliver financial services
through the mobile phone network. Despite the success of mobile money, there is
a lack of quantitative studies that unveil which factors contribute to the
adoption and sustained usage of such services. In this paper, we describe the
results of a quantitative study that analyzes data from the world's leading
mobile money service, M-Pesa. We analyzed millions of anonymized mobile phone
communications and M-Pesa transactions in an African country. Our contributions
are threefold: (1) we analyze the customers' usage of M-Pesa and report
large-scale patterns of behavior; (2) we present the results of applying
machine learning models to predict mobile money adoption (AUC=0.691), and
mobile money spending (AUC=0.619) using multiple data sources: mobile phone
data, M-Pesa agent information, the number of M-Pesa friends in the user's
social network, and the characterization of the user's geographic location; (3)
we discuss the most predictive features in both models and draw key
implications for the design of mobile money services in a developing country.
We find that the most predictive features are related to mobile phone activity,
to the presence of M-Pesa users in a customer's ego-network and to mobility. We
believe that our work will contribute to the understanding of the factors
playing a role in the adoption and sustained usage of mobile money services in
developing economies.
