Algorithms for Inclusion : Data Driven Lending for Women Owned SMEs
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. Financ...
| Main Authors: | , , , , |
|---|---|
| Format: | Brief |
| Language: | English en_US |
| Published: |
World Bank, Washington, DC
2017
|
| Subjects: | |
| Online Access: | http://documents.worldbank.org/curated/en/492891498813444777/Algorithms-for-inclusion-data-driven-lending-for-women-owned-SMEs http://hdl.handle.net/10986/27480 |
| id |
okr-10986-27480 |
|---|---|
| recordtype |
oai_dc |
| spelling |
okr-10986-274802021-05-25T10:54:41Z Algorithms for Inclusion : Data Driven Lending for Women Owned SMEs Alibhai, Salman Achew, Mengistu Bessir Coleman, Rachel Khan, Anushe Strobbe, Francesco ACCESS TO FINANCE MICROFINANCE GENDER EQUITY SMEs MICROENTERPRISE COLLATERAL DATA ANALYSIS 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. 2017-06-30T14:10:30Z 2017-06-30T14:10:30Z 2017-06 Brief http://documents.worldbank.org/curated/en/492891498813444777/Algorithms-for-inclusion-data-driven-lending-for-women-owned-SMEs http://hdl.handle.net/10986/27480 English en_US CC BY 3.0 IGO http://creativecommons.org/licenses/by/3.0/igo World Bank World Bank, Washington, DC Publications & Research Publications & Research :: Brief |
| repository_type |
Digital Repository |
| institution_category |
Foreign Institution |
| institution |
Digital Repositories |
| building |
World Bank Open Knowledge Repository |
| collection |
World Bank |
| language |
English en_US |
| topic |
ACCESS TO FINANCE MICROFINANCE GENDER EQUITY SMEs MICROENTERPRISE COLLATERAL DATA ANALYSIS |
| spellingShingle |
ACCESS TO FINANCE MICROFINANCE GENDER EQUITY SMEs MICROENTERPRISE COLLATERAL DATA ANALYSIS Alibhai, Salman Achew, Mengistu Bessir Coleman, Rachel Khan, Anushe Strobbe, Francesco Algorithms for Inclusion : Data Driven Lending for Women Owned SMEs |
| description |
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. |
| format |
Brief |
| author |
Alibhai, Salman Achew, Mengistu Bessir Coleman, Rachel Khan, Anushe Strobbe, Francesco |
| author_facet |
Alibhai, Salman Achew, Mengistu Bessir Coleman, Rachel Khan, Anushe Strobbe, Francesco |
| author_sort |
Alibhai, Salman |
| title |
Algorithms for Inclusion : Data Driven Lending for Women Owned SMEs |
| title_short |
Algorithms for Inclusion : Data Driven Lending for Women Owned SMEs |
| title_full |
Algorithms for Inclusion : Data Driven Lending for Women Owned SMEs |
| title_fullStr |
Algorithms for Inclusion : Data Driven Lending for Women Owned SMEs |
| title_full_unstemmed |
Algorithms for Inclusion : Data Driven Lending for Women Owned SMEs |
| title_sort |
algorithms for inclusion : data driven lending for women owned smes |
| publisher |
World Bank, Washington, DC |
| publishDate |
2017 |
| url |
http://documents.worldbank.org/curated/en/492891498813444777/Algorithms-for-inclusion-data-driven-lending-for-women-owned-SMEs http://hdl.handle.net/10986/27480 |
| _version_ |
1764465007252209664 |