Data Architecture of Branchless Banking
The series aims to explore the landscape of supply and demand-side data gathering efforts with the related goals of (i) identifying gaps in the data architecture; (ii) moving toward consensus – where it makes sense – on the correct indicators and methodologies to track progress and understand client value; and (iii) developing a common agenda for data collection and measurement as the branchless banking and mobile money industry continues to mature and improve. The series coincides with the recent release of the data by the IMF collected through its Financial Access Survey (FAS) and will include contributions from the Bill and Melinda Gates Foundation, CGAP, GSMA, Intermedia, MIX and UNCDF. We hope to also gather the perspectives of other members of the field through discussion on the blog.
How can we build a framework for a data architecture on the branchless banking industry?
The GSMA Mobile Money for the Unbanked programme (MMU) has been following the growth of the industry for the past few years using its Deployment Tracker which monitors the number of live and planned mobile money services for the unbanked.
There are at least four lessons that branchless banking stakeholders can take from the MFI experience when it comes to reporting standards.
The second post in our series described the importance of demand-side data for understanding consumers and their financial habits and needs. Various organizations are contributing to the global pool of demand-side data in branchless banking and in this post we’ll focus on two of the main sources. The Financial Inclusion Tracking Surveys (FITS) are annual household panel surveys in Uganda, Tanzania, and Pakistan while the Tanzania Mobile Money Tracker Study (TMMT) uses quarterly surveys to track market trends. Both are being carried out by InterMedia and the Bill & Melinda Gates Foundation. In this post, we’ll highlight some of the analysis on rural and urban households to demonstrate the actionable insights that can be gathered from such datasets.
The second post in a series on the emerging branchless banking data architecture focuses on the demand side of the data equation and attempts to answer questions such as: which clients are using which products for which purpose? What aspects of a service are they satisfied or dissatisfied with? And, perhaps most importantly, is the service having a positive impact on their general well-being?