NB Financial Health
Taking Financial Inclusion Data to the Next Level: National data is useful – but it’s not enough
In the era of “big data,” collecting facts, figures and numbers is seemingly second nature. For those of us in the financial inclusion industry, donors and investors ask how we will measure, learn and optimize; supporters want to understand the impact that we are having; and in-country stakeholders need to monitor progress.
Yet during the tedious process of collecting, cleansing and analyzing these thousands of data points, we often forget that data has another job to do: to inform and enhance our decision-making so we can move more people from unbanked to banked in the most effective manner. For that reason, as the financial inclusion community continues to expand our capabilities in data analytics, we must reexamine how this data can guide our actions.
Existing data platforms, including the World Bank’s Global Findex, are beginning to fill the gaps in national measures of financial inclusion by painting a comprehensive picture of the landscape. Surveys like the IMF’s Financial Access Survey and FinMark Trust’s FinScope survey contribute a level of depth to various indicators and add “color” to the picture. And national regulators that are committed to compiling and disseminating financial access data, like Peru’s Superintendencia de Banca, Seguros y AFP, provide detailed brushstrokes that bring the picture to life.
These different data aggregators paint a picture of financial inclusion around the world that provides the information necessary for key industry actors – policymakers, regulators and operators – to better understand the opportunities and challenges in their respective countries. Yet data at the national level can only provide a picture of low resolution in terms of the situation on the ground. In order to guide specific and targeted actions, the next iteration of financial inclusion data needs to drill down to the subnational level to increase clarity around financial service access, usage and quality.
For example, in order to expand service delivery, operators must identify the most promising, or pressing, market opportunity. Before investing in expansion, an operator must identify viable markets by answering questions about market demand: How many unbanked households live within this state? How many financial service access points currently exist in this state? Does the type of service point we offer do well in particular towns? Subnational data, including state-, district- and township-level data, will allow operators to zero in on specific opportunities to provide financial services in underserved areas.
Similarly, policymakers need disaggregated data to create effective and targeted policy. Vast differences between states or provinces necessitate tailored approaches; understanding which regions within a country are falling behind the national average can help focus efforts. Increasing the granularity of financial inclusion data, and overlaying it with other indicators or datasets, can help policymakers identify unique constraints that exist within particular states or districts.
With this in mind, MIX has sought high-quality data at the subnational level. Through our FINclusion Lab platform, supported by The MasterCard Foundation, users can visualize multiple data sets at once to better understand the key challenges and opportunities for expanding financial inclusion. Geo-spatial functionality maps out nearly 700,000 financial service points across 20 different countries including bank branches, mobile money agents, post offices and pawnshops. The data also includes more than 13,000 microfinance service points and 180,000 ATMs across three continents.
Yet, even with this breadth of information, our State of the Data 2015 report shows that there are still gaps in financial inclusion data that need to be addressed. For example, the report – which takes stock of which data points are available for measuring financial inclusion across 20 countries – concludes that, while financial service usage is a key measure of inclusion, this kind of information is very limited. Additionally, the report highlights an important opportunity to support local stakeholders in building data collection systems that can benefit the inclusive finance community at large.
By assessing the state of financial inclusion data, we at MIX hope to encourage policymakers, regulators and financial service providers to improve data availability, especially at a subnational level. An increased commitment to compiling and sharing data will allow industry actors to accurately identify obstacles, uncover new delivery channels and more effectively target investments – all to the benefit of the unbanked.
Nikhil Gehani is the Marketing and Communications Manager at MIX, a nonprofit data analytics organization that promotes financial inclusion through data and insight.