Pagaya Stock Uses AI To Power Asset-Backed Securities



About three months ago, we revisited a fintech company called Upstart (UPST) after the surge in its share price. In our previous article on the company, which uses artificial intelligence to analyze loan applications, we decided that there was too much risk and volatility to jump on the IPO, despite the history of impressive growth. What goes up must come down in the wacky world of hype: Upstart almost fell -40% since our last article although it is still relevant + 445% in its first year of trading. This happened despite a monster quarter that exceeded analysts’ expectations (for what it’s worth). Still, those same analysts punished the company for less than spectacular fourth quarter guidance. This is what happens when institutional investors pay attention to arbitrary price targets pulled out of the asses of people trying to become MDs before they turn 40.

So it is, as Mr. Kurt Vonnegut would say. Around the same time our article came out in September, another company that uses machine learning and big data to disrupt credit and debt lending industries have announced that it will go public – wait – by merging with a sspecial pgoal apurchase vscompany (After-sales service). Does Pagaya stock represent a reboot on an Upstart-like opportunity for retail investors?

About Pagaya Stock

Founded in 2016, the Israeli company is headquartered in New Yawk City (where a shortage of cream cheese threatens to bring the economy to its knees). Pagaya collected more than $ 221 million over the past five years, including from a number of investors who would understandably be interested in making bigger profits by lending money to more people in transactions than even your loan shark. corner might balk at doing. Some of the more well-known names include Citibank and supplemental insurance company Aflac, as well as other banks in Thailand and Israel, as well as financial services companies. SPAC de jure is EJF Acquisition Corp (EJFA), whose pre-merger share price rose only 2% when the deal was announced and still trades below the default $ 10 share price for PSPCs.

The founders of Pagaya
If all the fintech AI didn’t work, Pagaya would make a big name for a Swedish synth group as well. Credit: Pagaya

The deal will pay almost $ 500 million into Pagaya’s bank account and earn him a valuation of just over $ 9 billion, assuming no one withdraws their money sooner. That’s quite a jump from June 2020, when the company last raised $ 102 million Series D funds that valued it at $ 2 billion. The assessment may not be entirely without merit, as the company just reported third-quarter revenue of $ 137 million, which would give it annualized revenue of $ 548 million. . Using our simple valuation ratio (market capitalization / annualized income), Pagaya comes in at around 16 – well below our threshold of 40. The word ‘profitability’ was also used during the investor presentation, but if any benefits do exist they have not been specified – a typical obfuscation from SPAC.

How Pagaya makes money

Speaking of slightly confusing and opaque: let’s talk about how Pagaya makes money. As we mentioned earlier, the company uses AI to analyze consumer data using information that isn’t typically applied to traditional credit scores. We have already called this “the new credit score”. The idea is that by using tons of alternative data that old-fashioned credit bureaus ignore, algorithms can free up more capital by better quantifying risk versus reward. Many companies use alternative data not only to determine credit risk, but also for other types of financial and investment services, not to mention keeping Chinese citizens online.

Credit: Pagaya

In the case of Pagaya, its platform uses more than 16 million training data points and has already evaluated more than 17 million applications in markets ranging from personal and automotive loans to credit card and now to l ‘real estate. Based on second quarter results, network volume – the amount of loans processed by the platform – reached $ 4.7 billion in annualized terms. Pagaya partners with financial institutions like SoFi – another PSPC alumnus – to get their transaction flow.

Pagaya’s stakeholders – Credit: Pagaya

Pagaya receives commissions when its “network volume is acquired by institutional investors”. Indeed, the company sells asset-backed securities (ABS), which bundles these different types of consumer debt and sells them to investors. It’s kind of like an index fund based on the wants, dreams and silly ideas of the average person. In theory, algorithms can predict the likelihood that each loan will be repaid, presumably with an interest rate and terms that reflect the risk. There is no doubt that the platform also calculates a percentage of default, which is built into the pooled loans that investors buy. All of this is 100% automated – compared to around 70% with Upstart, which doesn’t aggregate loans this way – and Pagaya never issues a loan itself, which means it has no exposure. credit on the loans it generates for institutions. It also receives commissions on the assets created and managed by its partners.

In March, the company announced what it called the biggest consumer loan ABS – the $ 900 million PAgaya AI Ddebt (PAID) Confidence selection. In the past two years (from March 2021), Pagaya has achieved 10 of these PAYABLE trusts, all of which make full use of its owner AI.

Should you buy Pagaya shares?

Let that come into play: Algorithms are entirely responsible for billions of dollars in debt. You may remember a small economic meltdown around 2008 that involved hedge funds and banks creating a bunch of crappy mortgage-backed securities that left Wall Street on the run. This time, we’re asking the AI ​​to do better. From an emerging technological point of view, it looks really cool. From a risk aversion perspective, that scares us a bit. In its own long list of potential risks, Pagaya notes, “Our AI technology may not perform well or as we expect, which could cause us to inaccurately verify transactions. ” Oupsia.

The total addressable market is apparently the entire US market. Credit: Pagaya

Existential terror aside this company evokes, it’s just not a business model we’d want to be a part of, even if it’s light capital. Pagaya only makes money as long as consumers buy products and businesses provide people with credit. If that tightens – or even if institutional demand for consumer credit-linked loans dries up – Pagaya isn’t doing much business. Also, what incentive does Pagaya have to ensure that these are quality loans? He has already launched at least 10 of these ABS sets in just two years. There is no history of their performance over time. Those fine print in the SPAC game pretty much sums it up.

Our AI technology has yet to be thoroughly tested under downturning economics. If our AI technology does not accurately reflect a borrower’s credit risk under such economic conditions, loan performance may be worse than expected.

Credit: Pagaya SPAC Deck

Another red flag: shortly after the announcement of the proposed transaction, two US law firms would have became curious and wrote to shareholders of EJF Acquisition Corp. that the deal would dilute their shares and leave them less than 6% of the company. This could potentially derail the PSPC merger, although we never invest in a PSPC until the deal is done, and even rarely after that. The charge reflects quite well Remarks this week by Gary Gensler, the chairman of the SEC, when he said: “[R]Retail investors may not be provided with adequate information on how their shares may be diluted throughout the various stages of a SPAC. The SEC could finally take a step to bring a little more transparency to these PSPC transactions.


There is a good chance that IF Pagaya stock will go live on Nasdaq next year and initially take off based on the current growth trajectory. We are not going to let ourselves go, because the economic model seems too risky to us. It becomes a double jeopardy situation when you factor in the high risk assets that institutional investors acquire based on advice from an untested AI system. Wargames didn’t teach us anything?

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