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LendUp: the Knight disrupting Payday Loan industry

Through big data innovation, Lendup has turned the word "loan" into "opportunity" for those who are deemed "high-risk" by traditional banks

Photo of Iris Wang
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Today I’m writing about LendUp (https://www.lendup.com), a website and mobile app where people with poor credit can apply for payday loans.

Functioning as a direct lender, LendUp provides unbanked Americans with up to 1000$ short-term (30 days) loan within only 1 min processing time and with APY as low as 29%. The target audience here is huge: The U.S. government reports that 34.4 million people in the US are "unbanked" or "underbanked." Before LendUp, these borrowers had to go to predatory payday lenders and banks, suffering from low acceptance rate, long processing time, hidden fees, costly rollovers and high interest rates. Moreover, LendUp also provides opportunities for low-credit borrowers to get educated on finance and gradually build and improve credit. LendUp gives out not only loans, but also an opportunity to rebuild their life.


So how does LendUp make money then? Lending to such segment has inherently high risk and if LendUp doesn't charge high rate and fees, how will they survive? 


The answers lie in its innovative big data approach to accurately assess an applicant's possibility of paying back – it pairs creative data sources with smart algorithms.

From a data collection perspective, on the most basic level, LendUp asks for standard data from each applicant (including SSN). It also pulls in external data from both social networks (such as Facebook and Twitter) as well as public sources (credit scores and other data). More creatively, it looks at social media activity to ensure that factual data provided on the online application matches what can be inferred from Facebook and Twitter. Specially, LendUp looks for evidences to analyze the strength of applier’s social ties as an indicator for credit, assuming that the stronger the social tie is, the less likely the borrowers will default. Other behavioral data is collected too - as soon as someone comes to its site, the company is gathering data. Did you come from the site of a credit-building partner, or from a Google search for “fast money no credit check”? Did you immediately move the slider bars on the LendUp site to the maximum amount of money and maximum payback time, then hit “apply”? Every online action is monitored and analyzed to get better understanding of the person’s credit.


To me, the biggest difference in LendUp’s data approach is how targeted they are when collecting data. Unlike many other websites and companies who collect for as much as they can and then figure out a way to slice and dice, LendUp has a well-designed approach and only collects specific data for specific purpose. This creates efficiency and drives down cost, which is essential to LendUp’s value proposition. The tradeoff, of course, is potentially lower reliability since there is less data source. This is where the second advantage of LendUp comes in: a very smart machine learning algorithm. It helps LendUp fill in the gaps where certain variables might look bad, or where data is sparse for a particular applicant, by analyzing patterns across its user base.


In sum, with the help of big data innovation, it seems that the “do-gooders” can make a lasting profitable business too. Welcome to a new era of "Knighthood", where helping underprivileged doesn't necessary just mean giving out money for free.

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Photo of Marco

Fascinating post Iris... the use of behavioral data sounds very interesting indeed. How did they start?

Photo of Iris

I think right it's just an experiment to use the behavioral data as another way to triangulate. Not sure if they will reply too much on it tho - risk of ppl gaming the rules.

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