Zidisha (YC W14 Nonprofit) Changes Microfinance Equation(blogs.wsj.com)
blogs.wsj.com
Zidisha (YC W14 Nonprofit) Changes Microfinance Equation
http://blogs.wsj.com/venturecapital/2014/08/25/y-combinator-nonprofit-zidisha-changes-microfinance-equation/
6 comments
Great points vjvj! I'll try to answer them below:
1) Traditional microfinance services you are referring to have intermediaries (ground forces) to help them with loan disbursement, background check, loan recovery etc.. Zidisha is the first service to take everything online and that's why the interest rate is substantially lower. But it comes with a risk of low repayment rates. Repayment rate number was ~ 80% range last year and has gone up to ~90% after lots of process improvement. You can read more about it here: http://venturebeat.com/2014/08/20/y-combinator-backed-zidish...
2) Above link should answer it partially and Julia's answer should help more.
Hope it helps!
1) Traditional microfinance services you are referring to have intermediaries (ground forces) to help them with loan disbursement, background check, loan recovery etc.. Zidisha is the first service to take everything online and that's why the interest rate is substantially lower. But it comes with a risk of low repayment rates. Repayment rate number was ~ 80% range last year and has gone up to ~90% after lots of process improvement. You can read more about it here: http://venturebeat.com/2014/08/20/y-combinator-backed-zidish...
2) Above link should answer it partially and Julia's answer should help more.
Hope it helps!
I'd say the amount of interest charged by the average microfinance institution is a pretty good reason to want to reinvent the wheel, especially with impact studies often struggling to find compelling evidence of microlending having a net positive impact on clients. Borrowing from most Kiva institutions costs impoverished people with extremely unstable incomes far more than the credit cards us salaried middle class Westerners are advised not to binge on, and that's mostly because of genuinely high admin costs.
Zidisha's direct p2p is part of that picture, although at present lending at affordable rates without the overheads of any intermediary still results is default rates that are higher than the interest recovered. Another interesting feature of Zidisha is the willingness of some of its clients in sub-Saharan Africa to participate in discussions on the site and volunteer to help advise borrowers and follow up unpaid debts.
Ultimately, digital technology can make credit a lot more affordable in the developing world. For example Zoona (http://www.zoona.co.za/), which offers exceptionally low cost credit to tiny businesses in the developing world that are able to take payments via their app, is one of the most potentially disruptive innovations I've seen in payment tech.
Zidisha's direct p2p is part of that picture, although at present lending at affordable rates without the overheads of any intermediary still results is default rates that are higher than the interest recovered. Another interesting feature of Zidisha is the willingness of some of its clients in sub-Saharan Africa to participate in discussions on the site and volunteer to help advise borrowers and follow up unpaid debts.
Ultimately, digital technology can make credit a lot more affordable in the developing world. For example Zoona (http://www.zoona.co.za/), which offers exceptionally low cost credit to tiny businesses in the developing world that are able to take payments via their app, is one of the most potentially disruptive innovations I've seen in payment tech.
Companies like Kabbage (in the US) and Lenddo (Philippines, Colombia) use social graph data pretty extensively to predict credit risk. At Zidisha thus far, we're only using Facebook data to verify online identities of the applicants.
Lenddo has an API that some P2P companies are using. it is much richer than just Facebook.
I'm Julia, the founder of Zidisha. I'd be happy to respond to questions and comments.
This looks fantastic, it seems like you're doing some great work here. I was wondering if this 90% figure is based on the number of loans repaid (i.e. 9 out of 10 loans) or the amount of money repaid (i.e. $90 out of $100)?
The 90% figure is based on the amount of money repaid. More precisely, it's the amount that has been repaid for loans disbursed in 2014, divided by the amount that is due to have been repaid.
Hi Julia! A few questions:
* How much is Zidisha levered?
* A 10% default rate is very high. Can you get this down?
* How much is Zidisha levered?
* A 10% default rate is very high. Can you get this down?
I expect the default rate for loans being issued today will be less than 10%, because some of the amounts still outstanding will likely be repaid late. 90% is the portion of due amounts that has already been repaid (for 2014 loans).
That said, we are continuously improving our lending model in response to experience, and the repayment rate is increasing with each new cohort of loans we disburse.
Bayes Impact (another YC nonprofit which connects data scientists to nonprofits) is developing a credit risk prediction algorithm for use in vetting Zidisha loan applicants. Once that is deployed, we expect it will improve repayment rates substantially.
That said, we are continuously improving our lending model in response to experience, and the repayment rate is increasing with each new cohort of loans we disburse.
Bayes Impact (another YC nonprofit which connects data scientists to nonprofits) is developing a credit risk prediction algorithm for use in vetting Zidisha loan applicants. Once that is deployed, we expect it will improve repayment rates substantially.
Zidisha is working very hard to bring down the default rate. But as this is a relatively new approach, a lot of things can only be learned through experimentation. So far the results are encouraging and if this trend continues default rate should come down even further. You can read about some of the efforts here:
http://venturebeat.com/2014/08/20/y-combinator-backed-zidish...
Hope it helps and do share any feedback/suggestions you have. Thanks!
Hope it helps and do share any feedback/suggestions you have. Thanks!
Does Zidisha view having the broken (obviously because its not their first language and this is not a criticism of the borrowers) English in the descriptions as a benefit over descriptions that have been edited to be more readable? I could see arguments for both sides and wonder if they have experimented with giving a proofreader to every potential borrower, if only to improve readability. I'd imagine they haven't done it because it removes some of the human connections, but I'd be interested in A/B testing of it.
A good argument can be made either way, but I doubt we'd ever use a proofreader as so much of our identity is in keeping the lender-borrower communication direct and authentic.
That said, we do allow our users to post translations of non-English content, while leaving the untranslated versions accessible - you may view an example here: https://www.zidisha.org/microfinance/loan/SALIMATA2/9512.htm.... We also sometimes edit the titles of loan projects for clarity.
That said, we do allow our users to post translations of non-English content, while leaving the untranslated versions accessible - you may view an example here: https://www.zidisha.org/microfinance/loan/SALIMATA2/9512.htm.... We also sometimes edit the titles of loan projects for clarity.
Heh, looks like the same wording and effect I wrote on Kiva lender pages (e.g., http://www.kiva.org/lend/756762) to show the original language version :)
It'd be good to connect with you sometime on sharing knowledge on the tools used for allowing for user-based translations. I'm not sure if you saw the tools we use at Kiva, but I imagine there's some overlap where we could probably work together to create a more generic tool focused on translating loan descriptions that would work for both of us and other micro-finance organizations out there. There's some decent open-source stuff out there but nothing that's worked for us without heavy customization.
It'd be good to connect with you sometime on sharing knowledge on the tools used for allowing for user-based translations. I'm not sure if you saw the tools we use at Kiva, but I imagine there's some overlap where we could probably work together to create a more generic tool focused on translating loan descriptions that would work for both of us and other micro-finance organizations out there. There's some decent open-source stuff out there but nothing that's worked for us without heavy customization.
Congrats Julia and Zidisha! Great to see an awesome nonprofit leverage technology to change the game for microfinance.
"[B]orrowers in Kenya, for example, have a 10% annualized cost on Zidisha’s loans, compared to roughly 50% interest on typical microfinance loans in the country"
Out of curiosity, is there's no differentiation in interest rate based on risk models? Is this true direct lending, vs. what Kiva generally does (ignoring Kiva Zip)? What is your recourse if the loan defaults, and what kind of monitoring do you (can you) do without feet on the ground?
Out of curiosity, is there's no differentiation in interest rate based on risk models? Is this true direct lending, vs. what Kiva generally does (ignoring Kiva Zip)? What is your recourse if the loan defaults, and what kind of monitoring do you (can you) do without feet on the ground?
We actually don't set any interest rate. Zidisha charges a flat 5% fee per year the loan is held (for example a $100 loan held for three months would cost $1.25). In addition to that, we allow each lender to choose the interest to receive, if any - up to a maximum cap of 15%. If the loan is oversubscribed, the portions with the lowest interest are retained. The average rate chosen by lenders is around 5.5% per year, but there's a lot of variation, as borrowers with the longest positive repayment histories tend to attract lower interest rates.
Yes, this is true direct lending. In fact it's more direct than Kiva Zip, because the latter uses local organizations called "trustees" to help vet borrowers, and the trustees frequently post content in place of the borrower, especially outside the US. Zidisha does not use trustees or any other intermediary, and the content in the loan profile pages is written directly by the borrowers. You can view a sampling of the sort of comments our borrowers post here: https://www.zidisha.org/microfinance/testimonials.html.
Our best recourse against default is prevention, as it is very hard to collect a loan once it has become delinquent. Most of our credit risk protection happens at the application stage: things like a referral program that incentivizes borrowers to invite only trustworthy acquaintances to join Zidisha, machine learning tools to detect fraudulent accounts, and a credit limit progression system that incentivizes members to maintain high on-time repayment rates.
We spend a lot of time on fraud protection, but the most frequent cause of default at Zidisha is simple financial hardship. We're lending to a demographic whose income is not only low but also erratic, and lacks substantial savings, insurance or government safety nets. A big part of our credit risk protection is recognizing that reality and accommodating it through mechanisms like allowing borrowers to choose their own repayment schedules and adjust their installment amounts up or down to adapt to their financial situation.
Yes, this is true direct lending. In fact it's more direct than Kiva Zip, because the latter uses local organizations called "trustees" to help vet borrowers, and the trustees frequently post content in place of the borrower, especially outside the US. Zidisha does not use trustees or any other intermediary, and the content in the loan profile pages is written directly by the borrowers. You can view a sampling of the sort of comments our borrowers post here: https://www.zidisha.org/microfinance/testimonials.html.
Our best recourse against default is prevention, as it is very hard to collect a loan once it has become delinquent. Most of our credit risk protection happens at the application stage: things like a referral program that incentivizes borrowers to invite only trustworthy acquaintances to join Zidisha, machine learning tools to detect fraudulent accounts, and a credit limit progression system that incentivizes members to maintain high on-time repayment rates.
We spend a lot of time on fraud protection, but the most frequent cause of default at Zidisha is simple financial hardship. We're lending to a demographic whose income is not only low but also erratic, and lacks substantial savings, insurance or government safety nets. A big part of our credit risk protection is recognizing that reality and accommodating it through mechanisms like allowing borrowers to choose their own repayment schedules and adjust their installment amounts up or down to adapt to their financial situation.
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This is more evolutionary than revolutionary as far as microfinance is considered (for reasons below), and that's a good thing because there is no need to reinvent the wheel.
1) Repayment rates over 90% are not uncommon among existing microfinance lenders. Grameen Bank's rate is 97.6% - http://www.grameen-info.org/index.php?option=com_content&tas...)
2) Unlike mortgage providers who have a wealth of credit information for applicants, microfinance initiatives do not have this. Traditionally this has been solved by lending to groups and if any one person in the group defaults, the entire group is barred from future payments. This enables the MF lender to harness information held about individuals to screen non-creditworthy individuals.
It's a clever mechanism and I'd like to know more about how Zidisha uses facebook data to similar effect.