Snapshot 9: “Best Practices in Big Data Analytics” is one of 16 learning themes designed to address a range of topics within the digital finance space. The FiDA Partnership synthesizes, and disseminates the digital finance community’s knowledge of each of these learning themes as “Snapshots” that cover client, institution, ecosystem, and impact level topics. The Snapshots give a current view of “What We (in the digital finance community) Know” about the topic in question, highlight “Notable New Learning” and call attention to “Implications” for future research and investment.
Big data is a big buzzword in the digital finance community. In Snapshot 9, the FiDA Partnership explores this buzz and the potential of big data analytics in digital finance. The research indicates that as more people weave digital technology into their lives, digital finance providers can use the data generated from these digital interactions (i.e., big data) to create tailored products that better address customers’ financial needs and draw in new customers previously excluded from formal financial services.
The key to success in providing digital financial services is granular level data on customers that explain their needs. Big data has the potential to provide this granularity. According to the IFC Handbook on Data Analytics and Digital Financial Services, big data generally has five characteristics — veracity, velocity, volume, variety, and complexity. In the world’s six biggest emerging economies — China, Brazil, India, Mexico, Indonesia, and Turkey — big data has “the potential to help between 325 million and 580 million people gain access to formal credit for the first time.”
Although big data has gained traction in developed economies, research suggests that digital finance providers in low-income countries lag behind their counterparts in systematically collecting and analyzing customer data. In fact, the GSMA’s “State of the Industry 2015” report found that just a little over one-third (39%) of mobile money providers tracked the gender of customers and only 40% of mobile money providers knew the urban/rural split of their user base. Even these simple metrics can provide valuable insights for providers that want to increase their user base or encourage active use among existing customers.
Indeed, Accion Global Advisory Solutions’ interviews with industry experts and practitioners found that large datasets may not be a first-order concern for many financial service providers working with low-income populations. Rather, many organizations’ most immediate challenge is learning how to leverage small data. Moreover, big data is only meaningful if it is mined and analyzed to produce deep insights about customers. This is not a trivial task. Typically, digital finance providers lack the expertise and capacity to fully leverage the potential of big data. And it is likely a bigger task still to convince senior management that using big data analytics to generate a nuanced understanding of low-income customers can have significant bottom line benefits. Snapshot 9 outlines the steps s digital finance providers can take and the resources they need to cultivate in order to embark on their data journey.
More recently, the FiDA Partnership conducted interviews with thirty Kenyan and Tanzanian organizations — including mobile network operators (MNOs), FinTechs, microfinance institutions, and banks — and found that big data analytics has not taken off in sub-Saharan Africa. This is unsurprising given the challenges organizations face. Similarly, we found that these providers do not exchange and profit from big data directly, rather they have focused on developing relationships on a case-by-case basis to provide analytics of traditional or big datasets. FiDA’s upcoming Focus Note on this research, “Big Data and Analytics: Is East Africa There Yet?,” will discuss the four factors that the organizations interviewed believe are constraining the rapid growth of big data analytics in sub-Saharan Africa.
Nevertheless, there are companies in sub-Saharan Africa that are leveraging big data analytics to offer sophisticated products to their clients, such as by analyzing mobile phone usage and behavior data to grant small amounts of credit to customers via a mobile wallet or airtime account. M-Shwari, a combined savings and loan product, was one of the first to experiment with this model in 2013. More recently in December 2017, M-Shwari announced that they will segment customers who repay their loans on time and have positive savings behavior. M-Shwari will offer loans at cheaper rates to “good” customers and offer a rebate fee for customers who repay on time. They also plan to explore whether these offers encourage people to “behave” better in terms of their repayments and whether new customers will be inclined to use the service. Moreover, big data analytics is now being employed in sectors adjacent to financial inclusion such as M-Kopa, a pay-as-you-go (PAYG) solar energy provider. M-Kopa uses data to design and tailor their services from developing a price point to, more recently, extending a credit product to their customers.
While the big data and analytics space is still new and evolving, Snapshot 9 provides initial guidance to digital finance providers that are thinking about utilizing big data analytics and considering how best to navigate this path. Read Snapshot 9 to learn about the internal capacity and skills needed to pursue big data, see examples from organizations that have integrated big data analytics with their product development, and to find the top-10 reads in this space.