“Ants Dressed as Elephants”-Using data wisely when moving into small enterprise

We have worked closely with many microfinance institutions (MFIs), and found that as they move toward larger loan sizes, they claim to be lending to SMEs. Some caution, however may be warranted.
In a paper we worked on with the IFC in 2014 we found that good data analysis and segmentation can be important to better understanding the extent to which an institution is actually in the SME market. The paper (available at this link) notes that because of the broad variation in regulatory definitions and firm sizes of MSMEs, common practice has moved into accepting a definition based on loan sizes for financial institutions lending to MSMEs. This simplification can distort results, however, potentially either over- or under-estimating business size. One Bolivian MFI noted that some VSEs are “ants dressed as elephants” referring to the likelihood that many microenterprises are being characterized as VSEs because of their loan size rather than their own characteristics of organization, employees, or asset or revenue size. In other cases, “elephants” might also be dressed as “ants” when there is no information about the total outstanding loans of an enterprise. One Bolivian MFI we visited for this publication noted that based on loan size, some 40% of clients were VSEs, however, using the government’s definition of VSE that takes into account sector, sales volume, assets and number of employees, only 17% of clients were defined as VSEs.

What this means is that MFIs might manage their risk inaccurately, as they assume their SME borrowers to have greater repayment capacity than they might. Many MFIs still work with rudimentary segmentation of their clients. The case of Bolivia highlights that it takes the regulator's insistence to push them to analyze their clients more deeply.  It would be hard to argue for more regulation in the sector, at least in Latin America, yet regulators are ever more watchful lately as they see microfinance as a possible hotbed of overindebtedness and abuse of low income clients.

Our approach is proactive rather than reactive, where client data quality is strengthened and its storage is handled flexibly to enable financial institutions to better understand their clients, and the risks lending larger amounts to them entail.