
How Bayes Impact is reducing fraud for microfinance nonprofit Zidisha - pyduan
http://www.bayesimpact.org/blog/defending-microfinance-with-data-science.html
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brandonb
Cool post. I thought this was the most interesting point:

"As an outside agency, we can’t (and shouldn’t) make this decision for
Zidisha. However, it IS our job to inform them well enough to make it. The ROC
curve is abstract and hard to interpret for this purpose, so we translated its
information into a plot that directly measures the trade-off at every possible
threshold value."

So many of the standard techniques in machine learning assume an objective
function that's different from what the end-user actually cares about. Good
practitioners like Everett know to translate between the algorithm's loss
function (e.g., squared error) and the end goal. I'm surprised there's not
more research to let ML algorithms optimize the ultimate loss function
directly!

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jkurnia
I'm the founder of Zidisha, and I can't thank the Bayes Impact team enough for
this project. Like most small nonprofits, we don't have the resources to hire
in-house data scientists. Bayes Impact brought data science expertise within
our reach for the first time, and they've had a transformative impact on
Zidisha.

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j4pe
I was really surprised to see that you're actually disintermediating
microfinance. Other crowdfunded/donor-choice orgs create profiles after a
borrower has received a loan, and pay any loans made to that profile into a
pool for future borrowers. I thought this was a scammy but necessary part of
the business model until I saw your new loan growth - very exciting
development in microfinance.

I'd be curious to see how BI can inform your strategy in areas like market
selection, which can be especially crucial in microfinance. Which region
should you enter next, and what type of borrower should you focus on first?
There's plenty of data out there for analysis, but maybe others' repayment
rates for models like lending circles may not correlate too closely with
yours.

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jkurnia
That's an interesting question. Thus far our market selection has been driven
more by practical constraints than sophisticated analysis. We've focused on
countries that have well-developed electronic payment systems (such as M-PESA
in Kenya). We also try to target places where average incomes are low enough
that loan amounts that seem small in wealthy countries can have life-changing
impact.

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jpdus
Great job with Bayes Impact and thanks for the interesting post.

"So moving from a 20% fraud reduction to 50% will block about 200 additional
fraudulent loans and also 200 honest loans. Is that okay?"

From the accompanying chart it looks more like ~700 additional honest loans
blocked?

Any information about which threshold they picked? Looks like a difficult
decision for a non-profit, from the chart it looks like about 15% of
applications have been fraudsters but model accuracy is obviously limited
given the available features.

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rrggrr
Great overview, and I for one would love to see code snippets interpolated
between the outcomes.

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fubu
I'm a fan of what Bayes Impact, but the article opening seems really naive
about what the financial situation is for a pretty big chunk of US residents.

>> _As a Westerner, getting a credit card is only slightly more complicated
than tying my shoes. My world is raining with opportunities to borrow money to
go to school, open a store, consolidate loans, or buy an iPhone 6._

A little under 10% in the US are without a bank. And the "Underbanked", people
have some type of banking but still use Money Orders and stuff like Payday
loans, is over 20%. I wouldn't say "Raining with opportunities" lines up too
closely with those people.

Like I said, it's great to see what Bayes Impact is trying to help, but just
needed to clarify a bit about the US.

[https://www.fdic.gov/householdsurvey/2012_unbankedreport.pdf](https://www.fdic.gov/householdsurvey/2012_unbankedreport.pdf)

Edit: Updated to clarify that this was directed to the tone of the article
opening, not the author. I had previously used the word "author" instead of
"article"

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pyduan
It is true financial access is a problem even in developed countries like the
US -- though it is nothing compared to the situation of people in developing
countries (and I would say calling the author naive is an unnecessary
stretch), you are right in that it's important to raise awareness about the
underbanked in the US as well.

You'd be glad to hear we are actually working with other financial
institutions in the US like Opportunity Fund that provides microloans to
Californians. At Bayes Impact, we have a commitment to building repeatable
processes -- the good thing with using data to tackle problems is that it
allows us to benefit from economies of scale when working with different
actors that are facing similar problems.

~~~
fubu
I didn't mean to come across as saying the author was unaware, partly because
I'm positive the author is extremely well versed in the financial industries
within the US. I meant to relay that it "seemed" that way from what was
written to open that article.

I should have clarified more explicitly that I was discussing the way the
article appeared and not trying to make a statement about the authors aptitude
or understanding of the US situation. My apologies to the author.

