How Bayes Impact is reducing fraud for microfinance nonprofit Zidisha
bayesimpact.org
bayesimpact.org
"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!
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.
So I'd love to learn if people know more about how all this works!
Having lived the last year and a half in South America I really believe in the power of micro finance as a tool against poverty. It's really eye-opening to see from up close how difficult/impossible it is to escape poverty without access to credit.
Good luck with the project!
"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.
>> 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
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"
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.
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.