Affirm wants its data to tell a more complete customer story
Affirm has introduced a new transformer-based underwriting model aimed at enhancing its BNPL lending capabilities. This innovative approach will be implemented across its U.S. checkout process and is designed to better analyze patterns in consumers' credit histories, especially those elements that traditional models often overlook, such as the timing and sequence of credit activity.
By focusing on internal data, Affirm seeks to refine its lending strategy, potentially capturing consumers who may have been underserved by existing underwriting practices. This shift signifies a deeper commitment to utilizing advanced data analytics to create more inclusive lending options and improve customer engagement.
Key takeaways
- ▸Affirm's new model will be deployed across its U.S. checkout process.
- ▸The underwriting model focuses on unconventional patterns in consumer credit histories.
- ▸This initiative aims to improve lending options for underserved customers.
- ▸Affirm is investing further in data analytics to enhance customer engagement.
Why this matters
The adoption of this advanced underwriting model positions Affirm to better serve a broader customer base by identifying creditworthy consumers who may not fit traditional lending criteria. This could give Affirm a competitive edge in the crowded BNPL market by enhancing customer retention and attracting new users, while also potentially reducing default rates associated with inadequate credit assessments.