Lenddo: Increasing Financial Inclusion in Emerging Markets through Social Media
Lenddo has been highly effective in revolutionizing microfinance through its social media-based credit rating system; now, it鈥檚 taking its algorithms to a broader set of applications. With offices in Manila, Bogota, Mexico City and New York, Lenddo has assisted more than 500,000 individuals in emerging markets since it was founded.
Lenddo has been highly effective in revolutionizing microfinance through its social media-based credit rating system; now, it鈥檚 taking its algorithms to a broader set of applications. With offices in Manila, Bogota, Mexico City and New York, Lenddo has assisted more than 500,000 individuals in emerging markets since it was founded.
Lenddo was launched in Philippines in 2011 with an early focus on providing relatively modest loans to people who traditionally had difficulty accessing financing from traditional banks and lenders. It has since expanded operations to Colombia and Mexico, having identified 35 more countries where an emerging global middle class of Facebook users number a collective 1.2 billion.
Instead of using traditional methods to evaluate credit-worthiness, Lenddo developed its own algorithm to evaluate the risk attached to each borrower. By combining community-based microfinance techniques with social media presence and 鈥渄egree of engagement with their online communities,鈥� Lenddo can calculate not only the borrowers鈥� ability to repay a loan, but also her propensity to repay. Every Lenddo user has a 鈥淟enddo Score鈥� between 0 and 1000 that is used to assess credit-worthiness and determine whether or not one can be trusted to pay back the loan.
There are a number of determinants that go into the Lenddo Score, including but not limited to:
- Strength of social contacts: When online friends will vouch for your trustworthiness, the odds that you will repay a loan increase
- Number of social media accounts: The more social media accounts you link to your Lenddo profile, the more likely you are to repay a loan
- Number of friends and followers on social networks: More friends and followers means increased propensity to repay loans
- Length of time active on social media: The longer you have been on social media, the higher the likelihood that you will repay your loan
In what is known as an applicant鈥檚 鈥渢rusted network,鈥� the contacts of a borrower vouch for his reputation and character. Once the individual has been approved for the loan, the trusted network is kept apprised of his payment history, with timely repayments increasing a person鈥檚 鈥淟enddo Score” as well as the score of his network. In emerging markets with a strong focus on community ties, this methodology encourages repayment.
Lenddo loans out amounts equivalent to up to one-month of a borrower鈥檚 salary, with repayment periods between 3 and 12 months. These loans must be taken for life improvement purposes: for example, sending relatives to school, paying for medical fees or moving closer to a job.
Lenddo generated income not only by charging interest on its loans, but also gained access to an increasingly large data set with which it could refine its algorithm.聽By continuously improving this algorithm, Lenddo was able to achieve a default ratio lower than 5% (better than average!) in areas that traditional banks felt were too risky to enter.
The initial business case – providing microfinance to those who did not traditionally have access to it 鈥� led Lenddo to create a proprietary algorithm that could be used in a host of other ways in emerging markets. Understanding the wider applicability of this technology, Lenddo made the decision to sell its existing loan book to BanKO, a lender in the Philippines.
The number of non-traditional lenders in emerging markets is increasing, and Lenddo, with its algorithm, is already a leader in applicant screening. Lenddo generates a revenue stream with none of the risk associated with providing financing.
The technology can also be used for a host of additional applications, including:
- E-Commerce: Providing certainty that both parties to a transaction are authentic, reducing online fraud
- Recruitment: Validating the background information of prospective employees in emerging markets can be slow and expensive; Lenddo is a quick, cheap alternative
- Online Dating: Lenddo functionality can provide an additional level of security to protect users and enable safe meet-ups
The shift to a technology-focused business model has allowed Lenddo to expand its algorithm to markets it had not previously reached. In November 2015, Lenddo and India-based LendingKart reached an agreement to provide Lenddo鈥檚 non-traditional credit scoring models to the Indian market.
Arjuna Costa, a partner at Omidyar Network, phrases it well: 鈥淟enddo鈥檚 platform addresses a crucial piece in unlocking full financial inclusion to people in emerging markets. By using non-traditional data to make thousands of creditworthy consumers discoverable to lenders for the first time, Lenddo is revolutionizing the way we reach, assess and ultimately extend credit to consumers with no formal credit histories in developing markets.鈥�
Sources:
[1] http://www.lenddo.com
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Seems like an incredible model! I’m really impressed with the other applications they’ve identified to leverage their technology, and that they’re expanding their revenue streams by licensing the technology in markets where they haven’t already established themselves. I’m curious to know how they’re able to enforce the “life improvement” requirement for loans from the core business, seems like it could be difficult. I’m also interested to know if they’re exploring the possibility of bringing this concept to developed markets, there are definitely trustworthy people with little access to credit in the US!
Great blog post! Have followed Lenddo for a while now but am a bit skeptical of the true robustness of their credit model. I know that they recently moved from a lender to purely leasing their credit model to third parties (at least in LatAm) so would be curious if they tailored the “social elements” of their algorithm to fit specific country dynamics.
Cool post! Wondering how strongly the fundamental relationships behind the criteria for the Lenddo Score are? Have you seen any data around correlation between strength of social contacts and # of social media accounts with ability to pay back loans? I know their default rate is quite low but is that mainly related to the “pride factor” that we’ve seen in some of our cases (e.g., Patrimonio Hoy, Caja Espana)?