Every minute of every day millions of users in Africa create digital data. This new data is creating opportunities for alternative “big data” to catalyze an expansion of financial services to low-income and hard to reach populations. FiDA’s Snapshot 9, “Best Practices in Big Data Analytics” and FiDA’s Focus Note, “Can Big Data Shape Financial Services in East Africa?” discuss the potential of big data analytics in financial services in greater detail.
Snapshot 9 focuses on the opportunities created by big data — such as the number of lending and insurance products that have launched across sub-Saharan Africa. Indeed, mobile credit services are leveraging usage and behavior data to determine which customers can be granted small amounts of credit (cash or airtime) over their mobile phones. However, the extent to which companies are using mobile operator data and other big datasets is not well known. The return on investment in big data analytics is another unknown quantity still. What types of data, particularly what types of big data, are being used? Are the benefits tangible — how do the results from big data compare to those of traditional underwriting methods? What costs and risks exist? Is a market for data developing?
The FiDA Partnership’s Focus Note, “Can Big Data Shape Financial Services in East Africa?” sets out to answer some of these broader questions. The Focus Note shows how 30 leading organizations — including Kenyan and Tanzanian banks, microfinance institutions, mobile network operators, and FinTech organizations — think about big data and analytics and how they use (and how they don’t use) big data.
FiDA’s research found that players do not exchange and profit from big data directly. Rather companies have focused on developing relationships on a case-by-case basis to provide analytics of traditional or big datasets. This has led to a slow (but steady) uptake of big data and analytics and, as a result, a more measured expansion of services leveraging alternative methods. According to the interviewed organizations, four factors constrain rapid growth in this sector:
- Lenders have a limited use case for third-party data. Traditional underwriting practices still dominate credit decisions and repayment data and are seen as the most accurate predictor of risk. Big data is mainly used to determine a client’s ability to pay, typically with reference to low-denomination, short-term loans.
Increasingly, lenders are exploring the use of big data to assess willingness to pay, however, there are limitations, including the ability to validate quality and authenticity. Marketwide data is typically neither structured nor detailed enough to be used in a meaningful way. However, one type of marketwide data — satellite data — is proving useful for several FinTechs.
- Organizations are pursuing partnership models in lieu of transactional relationships. For a variety of reasons, including compliance with laws and regulations, preserving customers’ trust, and maintaining an early competitive advantage, organizations are treading cautiously with customer data. As a result, relationships between players working in this field resemble partnership structures rather than transactional marketplaces. While the financial services ecosystem is evolving slowly in East Africa, the strategy behind partnerships allows players to explore what is possible with data and analytics on a case by case basis. Moreover, this partnership trend is likely to continue because MNOs need to rapidly build an ecosystem around digital wallets and establish partnerships with banks and FinTech players that can deliver a wider variety of digital financial services. In turn this ecosystem will generate more data that will enable the creation of better targeted products.
- Most of the organizations interviewed are still testing, refining, and experimenting with which datasets are most predictive as well as validating their analytical models. This has led some FinTech organizations — such as Apollo Agriculture, FarmDrive, and Tala — to become customer facing, at least temporarily. By working directly with customers these FinTechs are able to capture data with which to train and prove the predictive power of their models. The implication of this is that they have to fund the loans from their own balance sheets; as a result, the amount of capital available for lending limits the volume of data they can collect. Further, even in situations in which customer data has been shared, data analytics organizations have learned that integrating with a provider’s system requires tremendous time and effort and hampers the ability of FinTechs to scale. Banks and MFIs need to improve readiness as much as FinTechs need more runway to improve models and offerings.
- Most banks and FinTechs believe a business case that is strategy- and leadership-led is essential to the uptake of big data and analytics. To expand financial services to low-income or hard to reach customers, banks need to be part of the solution. However, for this to happen, banks will need to shift their perspectives on the breadth of the customers they would like to reach.
The absence of a marketplace — and the pivoting of pioneers who initially paved the way to develop the market — could indicate that it’s too early for big data plays. More likely, however, it demonstrates that a market will never emerge in the same way that it has in the United States. Progress will come gradually, and partnerships among mobile network operators, banks, and FinTechs will be crucial to success. Organizations will need to weigh the value of being a pioneer against the current market challenges outlined above.
As a complement to the Focus Note, FiDA has developed Profiles of Digital Finance Organizations Leveraging Data and Analytics—brief profiles of how the organizations that participated in the research are using data. The profiles categorize data sources (big data as well as traditional) across four functional categories:
- Individuals’ financial services use or history.
- Individuals’ digital interactions using a device.
- Other individual data, such as psychometric survey responses.
- Marketwide data, such as crop prices or satellite imagery.
We hope that readers will benefit from the candid details shared by the participants in this study, without whom these findings and knowledge outputs would be impossible.