Why financial volatility does not worry this fintech | Payment source

With many companies tightening their belts out of recession worries, New York-based fintech Pagaya is instead eyeing growth — a strategy that reflects its approach of digging through data to find opportunity that others might overlook.

Pagaya went public June 19 via merger with EFG Acquisition Corp., a special purpose acquisition company, or SPAC. Pagaya hopes to expand its services, which include the use of artificial intelligence and the use of payment data to help financial institutions manage credit.

The company broke into the financial news on Monday after its stock, which had fallen more than 60% since its initial public offering, suddenly jumped more than 300% on Fridayraise speculation of one short squeezeor a sudden exit by short sellers to cover their positions, similar to what happened with GameStop in 2021.

Pagaya would not comment on the share price, as on Monday had given back most of Friday’s gains. Gal Krubiner, co-founder and CEO of Pagaya, said that when the economy struggles, banks will tighten credit, which Krubiner argues creates an opening for Pagaya’s model which analyzes thousands of data points to generate tailored recommendations for customers’ credit decisions, ostensibly to identify borrowers who don’t meet a FICO score threshold but still qualify for a loan.

“AI can observe more data and provide more relevance to each consumer,” Krubiner said.

Pagaya has built a dataset of more than 50 million consumers that feeds spending into the company’s AI engine, according to Krubiner.

Pagaya was founded in 2016 with three people and now employs more than 800. It had about $475 million in revenue in 2021, which was up approx. 380% from 2020.

The company refers to itself as a B2B2C firm, which uses payment records and other data sources to inform lending terms. Partners include Ally, SoFi and Visa. In the run-up to going public, Pagaya hired Ashok Vaswani, former CEO of Barclays UK, to be its president. Vaswani is responsible for leading all commercial, risk, regulatory, compliance and legal efforts with banking partners.

Pagaya includes buy now/pay later payments as data points for its AI engine, and also adds more recurring payments beyond monthly utility bills and rent.

“It’s more accessible than just the payment at the end of the month. That’s one reason we partnered with Visa,” Krubiner said. “It gives a lot more insight into payment records when we look to the point of sale to collect data.”

Fintech lenders typically use more AI and machine learning than banks do, although banks have closed the gap in the later years. Using AI based on a potential borrower’s payment trends is not widely used for credit decisions, although there is a lot of interest in the concept, according to Craig Le Clair, vice president and principal analyst at Forrester Research.

“What you usually have now is a human filling in third-party data, or maybe they have an internal app that does the scoring, so it’s a loose aggregation,” Le Clair said.

Newer AI engines for consumer loans look at factors like email or social media-related activity in addition to credit performance. If email addresses or social media accounts remain the same over a long period of time, it’s a good sign, according to LeClair, to emphasize that this type of data is combined with other types of data, such as a pattern of bill payments or types. of goods purchased on an e-commerce site or a store.

An example pattern could be an increasing amount of payments for luxury goods, or a decrease in payments for luxury goods against the purchase of more basic needs that indicate concerns about financial stress, according to LeClair.

That data is then run through an AI program with data from hundreds of other borrowers with similar profiles to produce a likely outcome of granting credit to that applicant, according to LeClair. This can produce a risk profile that is different from what would be suggested by a credit score derived from credit card or loan payments submitted to a reporting agency.

“In retail banking, there are a lot of good credit opportunities that are missed because the loan applicant doesn’t have a record of being in debt and paying on that debt,” LeClair said.

As another example, a potential borrower’s payments for an expensive custom car wash can indicate financial health for consumer credit, according to Stewart Watterson, a strategic advisor in Aite-Novarica’s banking and payments practice.

If a track of payments for a single borrower suddenly stopped, there would be enough examples of other people stopping these types of payments to make a predictive assessment, according to Watterson.

“Traditional credit reporting tells you that something bad has already happened,” Watterson said. “Using payment data and AI, you can get a sense of a forward-looking propensity,”

Fighting bias

The advantage of AI – that it can understand trends that humans cannot see – has the downside of making it difficult for humans to explain a particular decision, especially to an applicant who has been rejected.

“The more you apply the complexity of machine learning, there are fewer people in an organization who understand how the decision is made that can explain it,” LeClair said.

There’s also the issue of bias in machine learning, which can worsen over time as the AI ​​system gathers more information that goes through the original program, LeClair said.

“Our history is biased,” LeClair said, adding that about 90% of programmers were white men until about two years ago when the diversity of university graduates in AI-adjacent disciplines began to improve. “This past of mostly white men creates a present challenge that is difficult to clarify.”

Rohit Chopra, director of the Consumer Financial Protection Bureau, has warned bias in AI -informed lending. The algorithms can never be free of bias and can result in unfair credit determinations, according to Chopra.

AI companies such as Zestwho have partnered with financial institutions, claim machine learning can be programmed to augment the data source in a way that reduces bias in lending decisions.

Pagaya said it currently uses AI technology to guarantee loans coming through its partners, adding that the technology complies with all fair lending rules and regulations, and the company ensures it by doing independent validation with reputable third parties.

“We also have a strong compliance culture to ensure our underwriting achieves fair and favorable results,” Pagaya’s PR office said in an email.