Wojciechowski of MicroBilt.
Panelists agreed that the need for alternative data and behavioral data began with the changing demographics and spending habits of next generation consumers, including Millennial and Gen Z digital natives who maintain thin-file or no-file credit histories. Millions of these Americans may be creditworthy customers, but according to traditional scoring methods they simply don’t exist.
In fact, Kevin King from LexisNexis shared research that there has been a 70% increase in the number of consumers who cannot be scored by FICO and Vantage.
“So, there are other data providers out there that are collecting data on Millennials and thin files,” said Wojciechowski. “That’s important for a couple of reasons. Number one is diversity and inclusion.”
According to Pearson, fair lending will be the most important issue for regulators in the coming years: “It's going to be different from what anyone has experienced before because it's not just going to be fair lending, it's going to be fair advertising, it's going to be fair fraud screening. It's going to be fair servicing.”
Data Analytics & Machine Learning Enable Explainable Credit Decisioning
Cotè explained how Accelitas enlists data analytics and machine learning to focus on explainable credit decisioning, expanding inclusivity through predictive sources generally uncorrelated to traditional credit data. He cited the recent webinar of a major Buy Now, Pay Later provider that augmented its existing scoring methods with alternative data and analytics, turning previously rejected “unknown” consumers into well qualified customers.
Panelists agreed that big questions remain, including whether consumers snap back to pre-pandemic spending habits, and how the economy reacts to the unprecedented government response.
“No one knows,” Cotè said. “But it's coming at us pretty quickly.”
To watch the full Lend360 session, click here.
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