Using the lender Upstart Network Inc. as a case study, the CFPB issued a “no action letter” to the firm, confirming that its machine learning and AI-powered scoring models led to more approved applications, lower APRs, and expanded access to credit for typically underserved consumers, based on age, race, ethnicity, and sex.
Firms like Upstart are using alternative data points to identify near-prime consumers who would likely be rejected based on traditional FICO-based factors. This includes contextualizing common dings such as late or missed payments and defaults, and including other data points such as bank records, employment history, and utility payments. Banks are not only seeing fewer losses, they’re gaining unprecedented access to a consumer segment that traditional scoring would have ignored completely.
So with lending, AI is also poised to revolutionize the collections segment of the industry. Pairity not only uses alternative data to identify a consumer’s ability and likelihood to pay, but also identifies the best way to approach each individual consumer — and when — to optimize the first and all subsequent contacts.
Pairity can also pair your agents with their ideal consumer “type” to give each contact the best chance of establishing rapport and an ongoing relationship. But before we even get to the first contact, Pairity can identify likely collectable accounts based on alternative data points, giving you an even wider pool of accounts to work than what you’d gather with traditional vetting.