After reviewing federal regulators’ traditional theory of redlining, we discuss the types of underwriting practices that are likely targeted by Director Chopra’s recent comments expressing concern about “algorithmic redlining”, examine how the use of machine learning (ML) underwriting models incorporating alternative data can be more inclusive than traditional logistic regression models and result in more approvals for protected class members and “credit invisibles”, and offer our thoughts on actions that technology and credit providers should take in response to Director Chopra’s comments when developing and using ML models.

Alan Kaplinsky, Ballard Spahr Senior Counsel, hosts the conversation, joined by Chris Willis, Co-Chair of the firm’s Consumer Financial Services Group.