In a sign of technology solutions making further inroads into Wall Street, Pagaya, a New York-based investment manager that uses artificial intelligence to securitize assets, closed its fifth asset-backed securities transaction worth $200 million today. 

The deal brings the total amount of assets under management (AUM) to $1.2 billion at Pagaya. The company is already among the top ten issuers of 2019, according to data from Finsight. 

Artificial intelligence is a broad set of technologies, including natural language processing and data mining, that is used to identify patterns and trends from millions of data points. It is among the latest set of tactics used by investment managers to chase alpha or active and positive returns for an investment. Along with blockchain, which promises faster transaction speeds, artificial intelligence is part of an eclectic array comprising FinTech, a discipline that seeks to redefine the financial-services industry.


But their adoption is hampered by the absence of knowledge about their uses. “The issue in AI is similar to blockchain: many large financial institutions lack of understanding and talent to fully embrace emerging technologies, or that they approach something without a tech-first mindset,” said Ed Mallon, chief investment officer at Pagaya, in an interview with Decrypt.

However, Pagaya seems to have generated considerable buzz in the industry. Figures for ABS issuance fell during the first four months of 2019. But Pagaya claims to have grown during this period. The company is focused on consumer credit as an asset class. “It’s been exciting to watch because it’s a very defensive asset class that many people are unaware of,” said Mallon. The fact that it has little sensitivity to interest rates is also attractive, he added.

How does Pagaya use AI?

Pagaya’s AI uses 2,000 data points per borrower credit file. This data is then combined with macroeconomic figures to generate a comprehensive picture of the borrower with respect to market trends. “The combination of that individual data and the macro factors gives us an incredible view of the borrower’s true health,” said Mallon. “Credit scores can only account for so much, and really they are a great way to share the history of a borrower—not their future ability to pay off a loan.” 

Mallon did not disclose criteria used in their evaluation process. But he did say that the mass of data used to evaluate borrower loans is “significant.” That mass of data is also useful for AI software in identifying loans that might be overlooked by conventional systems or human managers today. Mallon terms them as an “investment opportunity” for clients. 

“Those loans are the opportunity—it’s like developing new mining technology that can find gold where other folks digging with a shovel might miss it,” he said.


Pagaya claims to have increased its origination, or the ability to source deals, by 300 percent in the last year. “… [The origination] was driven by the demand for what only our technology can accomplish—consistently above-market returns,” said Mallon. By the end of 2020, it expects to attract $500 million worth of investment pipeline per month.

And while there are fears that AI solutions are not able to replicate the discernment and critical thinking skills of human managers, Mallon is optimistic about the prospects for artificial intelligence on Wall Street. “I believe we are just a few years away from all collateralized loan obligations (CLOs), mortgage-backed securities (MBS), and ABSs being managed by AI,” he said.

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