Building Up a Credit History Outside of Financial Institutions

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Posted on May 21, 2012

Ali Ndiwalana is Research Lead, AppLab Money Incubator, at Grameen Foundation Uganda.

Access to capital is cited as a major challenge facing small businesses in rural areas in developing countries. Financial-service providers overlook this segment partly because they lack appropriate information to help gauge risk or investment opportunities available in rural areas. But this information does exist, albeit outside of financial service providers. In this post, we discuss how triangulation of data from different sources may facilitate better risk analysis, and highlight how mobile network operators (MNOs) and other partners can play an important role in providing such data.

MNOs are sitting on a data trove of customer records (CDRs, airtime purchasing, mobile money and airtime credit behavior) accumulated over the last few years. With mandatory SIM-registration requirements across East Africa (which require all mobile phone owners to register their SIM by providing such details as name, date of birth, home address, a photo, etc.), MNOs can use this data to infer social relationships and provide insight into the fixity of clients, volume and regularity of transactions, and the behavior of potential clients.

A number of private sector companies have started to explore the use of such customer data to help financial institutions predict customer interest and ability to repay micro-loans. Comparing this data to the data provided by credit-reference bureaus on which financial institutions typical rely indicates the potential benefit of such data. According to the Bank of Uganda’s Annual Supervision Report, as of December 2011, the Credit Reference Bureau (CRB) in Uganda had only 669,114 individual records, compared to the millions of individual records available to MNOs. Some early-movers (like Faulu Kenya) are already working with Airtel Kenya to leverage parts of such data to offer short-term loans.

This data can be enriched using social infrastructure. MNOs have a big agent network of airtime sellers and mobile money agents who can be tapped as sub-agents for last-mile verification and approvals, as opposed to a financial service provider having to setup brick-and-mortar branch operations. For a particular niche (e.g., farmers in Uganda), partners like Grameen Foundation Applab’s Community Knowledge Worker (CKW) programme would be more appropriate.

CKWs are information intermediaries selected by a local community and trained by Grameen Foundation to provide information and extension services to smallholder farmers to improve their livelihoods, which largely depend on agriculture. This distributed network uses mobile phone applications to disseminate and collect agriculture-related information. CKWs already collect rich data about farmer assets, inputs and yields that can all be contextualized and verified to provide more information about the risks and opportunities of lending to such a customer segment in a specific location. Potential borrowers can be identified based not only on the quantity of their assets, but also on how efficiently they use their current assets.

A combination of such rich datasets can be used to effectively gauge risk and identify potential borrowers, who can be targeted for credit offers by financial institutions. For example, farmers could be provided with short-term credit to get through the “hungry season” without liquidating productive assets, or offered longer-term credit to help them invest in better use of inputs at planting. Other institutions with an interest in serving niche users could also benefit from such data; the key is protecting customer privacy and empowering them to opt-in to any such offers.


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