Summary: CFPB Request for Information Regarding Use of Alternative Data and Modeling Techniques in the Credit Process

Consumer Financial Protection Bureau (CFPB)
Prepared by NASCUS Legislative & Regulatory Department
April 2017

The Consumer Financial Protection Bureau (CFPB or Bureau) would like to encourage responsible innovations that could be implemented in a consumer-friendly manner to help serve populations currently underserved by the mainstream credit system.  As such, the Bureau is seeking information about the use or potential use of alterative data and alternative modeling techniques in the credit process.  Specifically, the Bureau seeks to learn more about current and future market developments including existing and emerging consumer benefits and risks, and how these developments could alter the marketplace and consumer experience.  Additionally, the Bureau would like to learn how market participants are or could be mitigating certain risks to consumers, and about consumer preferences, views and concerns. 

The Bureau is requesting comments from all interested members of the public.  Comments are due by May 19, 2017.  You can find the Notice and Request for Information here.

Summary:

The notice defines “alternative data” as any data that are not traditional.  Traditional data refers to data assembled and managed in the core credit files of nationwide consumer reporting agencies and that includes tradeline information, credit inquiries, information from public records such as public records relating to civil judgments, tax liens and bankruptcies. 

Possible examples of “alternative data” include but are not limited to the following:

  • Data showing trends or patterns in traditional loan data
  • Payment data relating to non-loan products requiring regular payments such as telecommunications, rent, insurance or utilities.
  • Checking account transaction and cashflow data and information about a consumer’s assets, which could include the regularity of a consumer’s cash inflows/outflows or information about prior income or expense shocks
  • Data that some consider to be related to a consumer’s stability such as information about the frequency in changes in residences, employment, phone numbers or email addresses
  • Data about a consumer’s educational/occupational attainment, including information about schools attended, degrees obtained and job positions held
  • Behavioral data about consumers such as how consumers interact with a web interface or answer specific questions or data about how they shop, browse, use devices or move about their daily lives
  • Data about consumers’ friends and associates including data about connections on social media.

The Bureau is also aware of “alternative” modeling techniques that firms are using of contemplating such as:

  • Decision trees
  • Artificial neural networks
  • Genetic programming
  • “Boosting” algorithms
  • K-nearest neighbors

The Bureau identifies a number of potential consumer benefits and risks associated with the use of alternative data and modeling techniques in the credit process which are highlighted below.

Possible Consumer Benefits:

  • Greater credit access
  • Enhanced Creditworthiness Predictions
  • More timely information
  • Lower costs
  • Better service and convenience

Potential Consumer Risks:

  • Privacy concerns
  • Data quality issues
  • Lost transparency, control and ability to correct data
  • Harder to change credit standing through behavior
  • Harder to educate and explain
  • Unintended or undesirable side effects
  • Discrimination
  • Other possible violations of law (ECOA, FCRA, UDAAP)

Questions Posed:

The RFI request public feedback on a number of topics, which are listed below.  The specific questions asked can be found in Part D of the RFI found here.

  • Alternative Data
  • Alternative Modeling Techniques
  • Potential Benefits/Risks to Consumer and Market Participants
  • Specific Statutes and Regulations