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

After discussing the current state of the regulators’ knowledge about artificial intelligence and machine learning (ML) in underwriting models, we examine the regulators’ key areas of focus for ML models (explainability/accuracy in adverse action notices, potential hidden bias, testing for disparate impact), discuss how to test for and counteract disparate impact and how to search

In this podcast, we are joined by Scott Ferris, CEO of Attunely, a provider of machine learning (ML) and artificial intelligence (AI) technology to the debt collection industry.  We look at how changes in consumer behavior have impacted collections, technology’s role in collections, state law’s/GDPR’s impact on ML/AI and compliance strategies, how ML/AI can improve

A new CFPB blog post titled “An update on credit access and the Bureau’s first No-Action Letter” provides a boost to lenders using alternative data and machine learning in their underwriting models.

The Bureau issued its first (and so far only) no-action letter in September 2017 to Upstart Network Inc. stating that the