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Data Analytics, Lending, and the Next 100 Million Borrowers
Extension of credit to people in developing markets has been a
long time challenge. Banks, of course, look to repayment history to
make such determinations but in much of the world, banking
relationships and repayment track records are few. But history has
demonstrated that extension of credit in developing markets can be
effective and profitable. Just look at the Grameen Bank’s high
micro-loan repayment rates.
To address this repayment data dearth, Lenddo.com developed a lending data set
in multiple developing countries, having gone into the lending
business just to generate the data it needed to tune its machine
learning capability. Lenddo then built its algorithms
that examine some 1,000 characteristics in the data drawn from
social, mobile, and other sources. This Payments on Fire
podcast with Lenddo.com’s founder Jeff Stewart takes a look at
lending in developing countries, social and mobile data sources,
and examines the algorithmic "black box" that is at the heart
of the company’s approach to making credit decisions in "thin file"
markets.
View all episodes
By Glenbrook Partners, LLC
4.8
4141 ratings
Extension of credit to people in developing markets has been a
long time challenge. Banks, of course, look to repayment history to
make such determinations but in much of the world, banking
relationships and repayment track records are few. But history has
demonstrated that extension of credit in developing markets can be
effective and profitable. Just look at the Grameen Bank’s high
micro-loan repayment rates.
To address this repayment data dearth, Lenddo.com developed a lending data set
in multiple developing countries, having gone into the lending
business just to generate the data it needed to tune its machine
learning capability. Lenddo then built its algorithms
that examine some 1,000 characteristics in the data drawn from
social, mobile, and other sources. This Payments on Fire
podcast with Lenddo.com’s founder Jeff Stewart takes a look at
lending in developing countries, social and mobile data sources,
and examines the algorithmic "black box" that is at the heart
of the company’s approach to making credit decisions in "thin file"
markets.
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