How Pagaya uses alternative solutions to decision on credit, enabling more financial inclusion

More than 100 million Americans or nearly 45% of the US adult population can’t get credit from mainstream financial institutions.

Financial institutions can work to ensure responsible lending and policy underwriting by evaluating risks associated with underserved consumers, including lower credit score groups, protected ethnicity groups, and low-income groups. Additionally, the recent banking crisis has fueled concern about a credit crunch that could lead to a major pullback in bank lending.

However, traditional credit scoring models alone don’t paint a complete picture, and as a result, many industry players have started to gravitate toward looking beyond the traditional metrics of determining credit risk assessment. To better serve the underserved and credit-invisible consumers, companies are eyeing alternative credit data to fill in the gaps and enable inclusion.

Alternative data APIs bundle information from multiple data sources and other statistics from the digital footprint of the borrower. Aggregating, organizing, and analyzing various data points in a particular order can provide broader insights into the creditworthiness of the borrower and the risk associated with lending to them.

For instance, Plaid helps mortgage lenders like SoFi and Better make informed decisions through quick connectivity to source data. Fiserv uses alternative credit data in collaboration with TransUnion to streamline its auto loan applications. In the SMB lending sector, Kabbage uses alternative data — including business volume, transaction volume, social media activity, and the business’ credit score — to work out the line of credit a business should receive.

Pagaya combines alternative solutions powered by artificial intelligence with traditional credit reporting databases. It provides simplified and automated risk assessment solutions to its partners – banks, fintechs, and auto lenders – to help them make better lending decisions. The firm analyzes hundreds of data points to generate tailored recommendations that allow its partners to make credit decisions for borrowers that may not meet a particular FICO cutoff.

Pagaya is working in tandem with TransUnion to gain a better understanding of various financial products and services. Leveraging partner data analyses and synthesizing a plethora of FCRA-compliant credit bureau data and market insights from TransUnion positions the firm to make better credit decisions for its partners. 

“Through the use of sophisticated data science, machine learning, and artificial intelligence, Pagaya is able to review many additional FCRA-compliant data points about each customer over time, providing a more holistic view to better determine their creditworthiness than traditional methods,” said Leslie Gillin, chief growth officer at Pagaya and former JPMorgan Chase global CMO.

How it works: The process starts when a consumer submits a loan application to a partner that is already connected to Pagaya’s network. In case the partner declines the loan, the application is sent to Pagaya for a second look. Pagaya then provides an automated and near real-time recommendation to its partner. If Pagaya recommends moving forward with the loan, the partner proceeds with it. On the back end, that loan is acquired by one of Pagaya’s institutional investors, who provides an upfront funding source for access to the asset ahead of time.

This allows Pagaya to enable its partners to quickly assess their loan applications, grow their database, and deepen existing customer relationships – without taking on incremental risk. 

“This is the value of our win-win-win model. Our partners win because they capture more customers and increase revenue with little risk. Customers win through access to financial products. Investors win as they diversify their investments and gain access to unique asset flows to deploy at scale without having to build their own capabilities,” continued Gillin.

More than 40% of loan applications evaluated by Pagaya represent Black or Latinx consumers, over a third of approvals represent a Black or Latinx consumer, and roughly half of the enabled approvals are below 660 credit score consumers – representing around 57 million households in the US. In total, roughly 2 out of 3 issued loans are low-to-moderate (LMI) households, enabling more underserved consumers to access the lending product of their choice.

“It’s at this time – the current economic climate – when Pagaya’s AI network becomes even more relevant, enabling consistent growth for our partners and investors. Our product is in greater demand by our partners, so we are continuing to serve their customers who still have lending needs, while we have the capability to handle the risk in line with market averages over time,” added Gillin.