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.

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.

Wondering this as well, I only recently learned that all these Kiva loans I'm participating in, helping to get it funded, had already happened! It kind of makes me wonder everytime my charity-team sends me a message 'only 5 more hours to get this loan passed!'. Not that I particularly mind, but my curiosity was activated as to how this works, why, if a different model can be better and what the benefits/drawbacks are.

So I'd love to learn if people know more about how all this works!

Clicking through on the article I ended up browsing through several of the projects on Zidisha and reading the founding story. Very impressive project!

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!

Have you thought about putting the (anonymous) data in a Kaggle competition?

We actually explored that before we met Bayes Impact. The Kaggle team thought a competition would be overkill for our data set (about 8000 loans).

Oh, I can see their point. I'm glad you were able to have some useful analysis done on the data anyhow!