When it comes to personal finance, FICO scores are a key part of the conversation. A simple summary credit score (between 300 and 850), it can determine, for instance, whether one may qualify for that trendy new rewards card or not. For others, it can make a difference in the interest rate they may have to pay on a home improvement loan. For the roughly 100 million Americans with FICO scores under 670 (considered “subprime”), however, it means much more: often the difference between having access to (any) credit at a traditional bank, or not . Avant, an online personal loan lender, is out to change that, and machine learning is at the heart of what it does.
Simply put, Avant has used machine learning to create its own credit scoring system, an alternative to FICO scores which are otherwise the industry standard. Particularly, Avant looks far beyond the handful of factors, such as credit history and utilization, that determine FICO scores, instead using advanced algorithms that consider more than 10,000 variables in evaluating a loan application . This helps Avant identify, and serve, customers among the “subprime” FICO pool that otherwise exhibit “prime” behavior (through the additional variables tracked). Furthermore, the data-driven and online nature of the process makes the loan application as easy as “hailing a car with Uber”, with approvals provided instantly or within a business day . Since inception in 2012, Avant has thus used machine learning to make >$4 billion loans to 600,000 customers .
But that wasn’t without its challenges. In 2016, the very credit scoring model at the core of Avant’s business came under fire for not being as accurate as expected. For instance, ~$300 million loans generated by Avant and sold to investors in April 2016 exhibited 14.5% net losses in first 11 months, significantly higher than the 10.6% projected loss rate . Besides financial loss, this also shook investor confidence, causing the company to slow down lending and focus instead on tightening the credit standards of its machine learning algorithms. Performance improved with loans generated the following year, and the company is expected to generate a profit in 2018 .
In the medium term, competition poses a risk as other players develop similar algorithms and catch on to the use of machine learning in banking previously underbanked “subprime” customers. In particular, the incumbents of the personal lending space, traditional banks, may seek to expand into Avant’s market and hence pose a threat. The company has anticipated this risk by instead initiating partnerships with traditional banks, offering its proprietary credit scoring algorithm as a SaaS product under the “Amount” brand . HSBC announced in October 2018 that it will soon start using the Amount platform to offer personal loans of up to $30,000, citing the expansion as “an area of opportunity … outside of our footprint” . The expansion from incumbent banks into this space thus represents both a threat and an opportunity, and active management by the company of this risk will be critical in converting it into an opportunity.
Looking ahead, regulatory compliance is likely to be a key area of focus for the company. Emerging from the 2008 financial crisis, traditional banks cut back on “subprime” lending, tightening access to credit for loan applicants that today form the business opportunity and customer base for lenders such as Avant. Marketplace lenders such as Avant are currently regulated more lightly than traditional deposit-taking banks . However, this is because machine-learning based lending is a nascent industry, and the regulation for it is only now developing. In 2016, the Treasury released a white paper on marketplace lending, requesting information from 28 online lenders including Avant . There is a risk of enhanced regulation in the future for Avant, especially if such marketplace lenders are eventually held to the same standards of consumer protection and financial stability as traditional banks. I would recommend the management to anticipate and prepare in advance for this risk, with similar foresight as it showed with regards to competition by developing Amount and partnering with banks.
Today, the use of machine learning in lending is still largely untouched territory. As the use of machine learning in financial services expands beyond the first movers we are seeing today, I wonder how competition would evolve: particularly, what barriers to entry would exist (if any), and what values among the customer offering would firms differentiate themselves on.
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- “What Are the Different Credit Scoring Ranges?” Experian. https://www.experian.com/blogs/ask-experian/infographic-what-are-the-different-scoring-ranges/. Accessed November 13, 2018.
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- Avant. https://www.avant.com/about_us. Accessed November 13, 2018.
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- Levitt, Hannah. “HSBC U.S. Partners With Web-Based Avant to Offer Personal Loans”. Bloomberg. https://www.bloomberg.com/news/articles/2018-10-22/hsbc-u-s-partners-with-web-based-avant-to-offer-personal-loans. October 22, 2018. Accessed November 13, 2018.
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