Algorithms for Inclusion
All over the world, women have less access to credit than men. Because of both discriminatory property laws and unwritten social customs, women are less likely than men to own high-value assets that can be used as collateral to secure loans. Financial institutions in developing countries rely on heavy collateral requirements because they don’t have enough information about their borrowers. New technologies - many emerging from financial technology (fintech) startups in the Silicon Valley - have the potential to generate data on borrowers that can replace traditional collateral requirements, and unlock finance for women. In Ethiopia, the authors explored introducing fintech that can harness the data that financial institutions are already sitting on. The technology focuses on digitizing hard-copy loan application files of previous borrowers to identify trends and characteristics associated with repayment, and predict creditworthiness of new borrowers. Fintech solutions can viably address the collateral constraint for women borrowers, and can work even in low tech environments. But technology adoption isn’t easy, and assessing the readiness of financial institutions to adopt fintech and embark on technological change is a critical first step.
| Main Authors: | , , , , |
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| Format: | Brief biblioteca |
| Language: | English en_US |
| Published: |
World Bank, Washington, DC
2017-06
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| Subjects: | ACCESS TO FINANCE, MICROFINANCE, GENDER EQUITY, SMEs, MICROENTERPRISE, COLLATERAL, DATA ANALYSIS, |
| Online Access: | http://documents.worldbank.org/curated/en/492891498813444777/Algorithms-for-inclusion-data-driven-lending-for-women-owned-SMEs https://hdl.handle.net/10986/27480 |
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