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Sasha Orloff, CEO and Co-founder, LendUp

Sasha Orloff headshot.JPG

Please give us a bit of background on yourself, and how your organisation plays a leadership role in the financial technology space.

As the CEO and co-founder of LendUp, I’ve dedicated my career to the many ways technology can expand access, choice, and transparency in financial services -- particularly for the underserved. I was inspired early on by Nobel Peace Prize winner Muhammad Yunus’ book, “Banker to the Poor,” which prompted me to move to Honduras to work for the world-renowned Grameen Foundation. What started as a six-month internship turned into years of service with the organization and its technology team. After Grameen, I worked for The World Bank’s Consultative Group to Assist the Poor, Citigroup, and then went on to start LendUp with my step-brother, Jake, in 2012. Jake brings the tech to our fintech equation, which is very much our secret sauce; he started his career at 16 years old as the 80th employee at Yahoo! and became a Platform CTO at Zynga by age 30.

At LendUp, our mission is to offer anyone a path to better financial health. We’re focused on serving the 56% of Americans who don’t have access to traditional financial services due to low credit scores or income volatility. There's a new, emerging class we've never seen before, as we have essentially gone from careers to jobs to being paid for gigs and tasks. We build products (credit cards and loans), technology, and educational experiences to serve the needs of this emerging middle class, which are unique due to increasingly volatile incomes and other “new” issues.

We’re able to tackle these new challenges in ways that many others can’t thanks to our tech, which is all built in-house -- unlike many other companies, even in the fintech space -- and because of our advanced statistical models. This combination means we aren’t as confined by the same systems as many other financial institutions, so we can offer borrowers better options -- and we can serve more borrowers who need and deserve credit, but whose credit scores may not be an accurate reflection of their credit worthiness. We’ve originated 4 million loans totaling more than $1 billion, we’ve saved our customers more than $125 million in fees and interest, and we do it all mobile-first since 98% of our borrowers come to us on mobile devices.

How well are financial companies adapting to the coming of age phase of FinTech development, specifically with regards to machine learning?

The industry has really reacted to these new and changing technologies, exploring many possible use cases. For our part, we've not just adapted, but we've helped forge this phase, and made it accessible to other financial institutions such as banks, to think about how machine learning can improve their analytical capabilities.

Every financial institution is by nature a risk management organization, where predictive analytics is the critical determinant of success. And as the world gets more complicated, with more data available about people, we can take a previously risky population -- where “risky” is defined as the lack of statistical confidence because there was limited data available -- and use that to identify those who are credit worthy, but whose traditional credit scores may not be “good enough” to qualify them for traditional financial services. Many people who were previously shut out of mainstream credit now can have access to the products they need. And we do it in a way that is safer and more affordable than products of the past, such as these shadow “fee harvester” credit cards or dangerous loans like traditional payday loans.

Readers of The Economist are likely aware of how AI and machine learning are becoming prevalent in the wealth management space, helping individuals and organizations invest their money. For LendUp’s customers, these AI-based techniques can be used in a similar fashion but for a different purpose. For example, one possible service could be to get the customer’s consent to look at their transactions and repayment behavior and use that information to derive and share feedback on their spending and payment habits, which could help them make decisions that would lead to the best possible financial outcome for them. This would help LendUp further achieve its mission of giving anyone access to better financial health by helping them become better credit card users -- something that’s especially important for our target demographic: those with poor or thin credit histories.

Other applications across the industry include areas like fraud identification and prevention, bots that allow organizations to interact with customers in a more efficient way, and many others. It’s exciting to watch as the industry continues to grow and change with new advances like AI -- and to see LendUp driving a lot of innovative change.

What are the key challenges you see for maturation and adoption of these new innovations in financial services?

There has been a lot of hype around AI and machine learning. The real risk is that while the possibilities are virtually endless, it takes the right talent and organizational focus to get it right. Many may promise innovative AI techniques, but don’t deliver. So there are a lot of large financial institutions that go down this path, only to be frustrated that they’re not getting the results they expected.

Here at LendUp, machine learning currently is one of our main areas of focus. It’s what allows our business model to work at such lower rates versus others in our space: it gives us the ability to better judge what factors contribute towards a borrower's repayment success, so that we can help more people start on a path to better financial health and access cheaper rates over time. As a fintech startup, we’re very good at the tech and innovation piece -- an area where larger banks have traditionally struggled. And we definitely believe in a partnership model. To that end, we’ve partnered with two banks to date, with conversations underway with several others. It’s a real win-win for both sides, and for borrowers.

What will you be discussing at The Economist's Finance Disrupted Conference?

I’ll be part of a discussion on how AI is impacting every corner of the financial industry. There is a lot of buzz about AI impacting all industries, and the financial industry is one that is particularly ripe for change -- and is just sooooo big. For LendUp specifically, this means how we help serve even more customers who are locked out of the financial mainstream and have trouble accessing socially responsible financial products. It means we can build on the traditional ways of determining whether a person is “credit-worthy” -- a process that is often hard to understand, unfair, and disproportionately affects young people and people new to this country. I’m excited to hear the other panelists’ perspectives!

What can you tell us about “fintech for good”? What about “machine learning for good”?

To me, fintech for good ultimately comes down to finding a way to better serve the needs of Americans. It’s about that 56% whose needs aren’t met by the current financial system.

When it comes to credit and lending, the cost of origination -- the process by which borrowers apply for credit and we evaluate the application -- is a key factor in our ability to do good. Working with people with subprime credit scores, or with those who experience income volatility, means we have to be very good at determining who is likely to be able to pay us back and who isn’t. So it’s critical that we develop extremely accurate underwriting models. We don’t want to approve borrowers who will default, as that high cost will get passed along to the next borrower. Instead, the smarter our underwriting model gets, the more we can save our customers -- and the more borrowers we can serve.

To learn more about the Finance Disrupted event, please click here.