Why Big Data Is a Big Can of Worms

Leveraging big data for financial inclusion is not as easy as it sounds. Here’s what FiDA has learned about the challenges financial service providers face.

> Posted by Maha Khan and Marissa Dean, FiDA Partnership

Big data is a big buzzword in the digital finance community. As more people weave digital technology into their lives, financial service providers (FSPs) can use the data generated from these digital interactions (e.g., mobile money transactions, SMS messages, mobile app activity, etc.) to create more tailored financial products and to attract new customers previously excluded from formal financial services. Accelerating Financial Inclusion with New Data, a joint report from the Center for Financial Inclusion at Accion (CFI) and the Institute of International Finance (IIF), discusses the types of data sources and tools that FSPs can use to better understand and serve low-income and hard-to-reach clients.

To understand how FSPs—ranging from banks and mobile network operators (MNOs) to Fintechs—use (or don’t use) big data analytics, the Mastercard Foundation Partnership for Finance in a Digital Africa (FiDA) conducted research in East Africa in late 2017. Interviews with a range of organizations suggest that these companies have been relying on analytics of traditional or big datasets, but there is a slow (but steady) uptake of big data and analytics. As a result, there has been an expansion of products and services leveraging alternative data sources. According to the interviews, four factors constrain rapid growth in this sector:

  • Lenders have a limited use case for third-party data
  • Organizations are pursuing partnership models in lieu of transactional relationships
  • Most of the organizations interviewed are still testing, refining and experimenting with which datasets are most predictive, as well as validating their analytical models
  • Most banks and fintechs believe a business case that is strategy- and leadership-led is essential to the uptake of big data and analytics.

Nevertheless, there are companies in sub-Saharan Africa that are leveraging big data analytics to grant small amounts of credit to customers via a mobile wallet or airtime account. For instance, M-Shwari, a combined savings and loan product, was one of the first to experiment with developing algorithms based on mobile behavior data in order to offer low-value credit to customers in 2013. In December 2017, M-Shwari announced that they will segment customers who repay their loans on time and have positive savings behavior.

However, FiDA’s interviews with financial services providers found that mobile behavior data has significant limitations as the data does not give a holistic picture of an individual’s spending or device behavior. But more specifically, MNOs don’t share behavior data freely, and for good reasons. Their perception of regulation on data sharing is that MNOs cannot give individualized information to third parties without consent from the customer and the regulator. Additionally, and perhaps most importantly, MNOs believe that selling raw customer data would erode their hard earned customer trust.

Moreover, as algorithms based on behavioral data become more prevalent, providers will find themselves constantly playing a game of cat and mouse with fraudsters who want to take advantage of the all-digital nature of these products in order to scale successful fraud schemes to high volume. This is the flip side of big data, which is normally thought of as a way to scale successful products to high volume.


Cropland in Maharashtra, India, mapped from satellite imagery. (Credit: Harvesting Inc.)

The absence of a marketplace to profitably exchange big data—for example, between fintechs and FSPs—could indicate that it’s too early for big data exchanges or transactions. Nevertheless, progress will come gradually, and partnerships among mobile network operators, banks, and fintechs will be crucial to success. At the end of the day, organizations will need to weigh the value of being a pioneer against the current market challenges. In fact, FiDA recently published a case study on satellite imagery in financial services, and will be publishing a second one (on psychometric assessments in lending) that presents the journeys of fintechs that have integrated big data in their business and product offerings, and how they have partnered with financial institutions to implement these analytics. FiDA is excited to see CFI and IIF explore the potential of big data and analytics in financial services and will keenly watch this space.


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