Digital consumer lending service Affirm has completed a $300 million Series F led by Josh Kushner's Thrive Capital, with participation from new investors including Fidelity Management and Research Company, Baillie Gifford and Wellington Management. Ashton Kutcher's Sound Ventures also participated in the round, which gives the company a $2.9 billion post-money valuation, according to Axios, up from the $1.8 billion reached with a $200 million Series E in 2017.

Affirm, founded in 2012, provides consumer loans issued during checkout on ecommerce websites. The "loans"—usually alluded to on the company's website with the more consumer-friendly phrase "pay over time"—are used to immediately pay merchants for a customer's purchase, similar to PayPal Credit, which the company claims increases average conversion rates, order value and customer satisfaction.
Max Levchin, CEO of Affirm
Affirm CEO and co-founder Max Levchin (pictured) also co-founded PayPal, though unlike PayPal, Affirm focuses on fronting purchases for consumers and doesn't provide broad financial services.
The San Francisco-based company has enjoyed a steady upward trajectory of success and growth. It took just over five years to evolve from a comparatively modest $23.8 million valuation shortly after inception to unicorn status. The company reports raising over $800 million in cumulative equity funding, while issuing over $2 billion in consumer loan volume in 2018.

Affirm's service attempts to improve dismal conversion rates in ecommerce by enabling consumers to make purchases with less friction. Most visits to an ecommerce website do not translate into paying customers, leading to single-digit conversion rates on average across most industries. Monetate, a VC-backed provider of ecommerce optimization services, reported a 2.4% average conversion rate among online businesses worldwide in 3Q 2018. US-based businesses performed worse, with only a 2.2% conversion rate, while Great Britain saw slightly better performance at 4%.

The number of visits relative to the comparatively small number of conversions results in losses via wasted advertising spend and less actionable consumer data recorded, among other challenges. While many factors play into improving conversion rates, ranging from shaving off milliseconds from a website's load time to improving brand image, customers ultimately experience friction when the topic of payment arises, leading to abandoned shopping carts and missed opportunities for merchants.

This is where Affirm comes in, as the company makes on-the-spot loans to cover purchases in lieu of a customer relying on access to a credit card or funds in a bank account. While this may not seem like a significant deciding factor for customers that have an abundance of financial resources, Affirm claims to use considerations beyond a traditional credit score and income figure to enhance customer approval rates. This, in turn, enables individuals to make purchases when they may have otherwise been declined for a credit card or may be awaiting a paycheck.

"When someone buys something, we aren't giving that person any money; if someone wants to buy, say, a mattress or bicycle, we pay the merchant that money," Elizabeth Allin, vice president of communications at Affirm, told PitchBook. "When you decide to buy a bike, we are looking at your credit history and looking at data and reading it differently than a traditional credit card would. ... Our algorithm doesn't just look at you as a borrower but also the item you are buying and uses data from millions of transactions and billions of dollars in volume to understand your risk for repayment."

Allin is alluding to a growing societal shift away from traditional credit scores to judge credit applicants. Rather than focus on a three-digit number that objectively scores past credit events, a "whole picture" concept that understands a person's life situation is entering the world of lending.

Other VC-backed startups, such as RevolutionCredit and Tala, are also working on implementing this idea to varying degrees of success, given the inherent difficulty in trying to understand a person's situation while also processing potentially millions of applications. This may have been the problem the modern-day credit score was trying to initially solve, but artificial intelligence and machine learning may advance the "bigger picture" concept moving forward.

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