Customers at an MTN kiosk | Photo Credit: Hans Olofsson
Digital credit often uses machine learning and “alternative” big data sources, such as mobile phone usage, to generate credit scores and distribute loans to consumers in an instant, automated, and remote fashion. Are these products distributing loans to low-income and financially underserved borrowers that would benefit from this financial service? What are the impacts—both positive and negative—of digital credit products in emerging markets, and do they differ by gender?
CEGA’s Digital Credit Observatory (DCO) hosted a panel on October 25 sharing rigorous evidence on the impacts of digital credit and discussing potential insights for implementation and regulation. You can find a recording of the discussion and related resources below.
The panel was part of the What Works Global Summit, an annual event for evidence professionals, development practitioners, and policymakers to discuss the latest evidence on development policies and interventions.
About the Event Since 2011, the World Bank’s Global Findex Database has been the definitive source of data on global access to financial services from payments to savings and borrowing. The Findex data and its insights have driven numerous applications in both research and policy fronts across...
On September 8th, CEGA’s Digital Credit Observatory (DCO) hosted Welfare Impacts of Digital Credit: Results from a Randomized Evaluation in Nigeria as part of the DCO Webinar Series. Researchers Joshua Blumenstock (UC Berkeley; DCO Scientific Director), Daniel Björkegren (Brown), and Suraj Nair...
On September 18th, CEGA's Digital Credit Observatory (DCO), as part of the DCO Webinar Series, hosted "Digital Credit: Filling a hole, or digging a hole? Evidence from Malawi." Jonathan Robinson (University of California, Santa Cruz; DCO Scientific Director), Pascaline Dupas (Stanford), and...
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