AI Credit Decisions and Adverse Action Notices Quiz

Answers and explanations appear as you take the quiz.

1. According to Regulation B's commentary, why is telling a denied applicant they failed to meet internal standards not enough?
   • It reveals confidential model details
   • Reasons must be specific and relate to the factors actually considered
   • It must be stated in at least two languages
   • Internal standards can only be disclosed by the FCRA notice
2. What did CFPB Circular 2022-03 say about complex credit models?
   • Complex models are banned for credit decisions
   • Creditors using complex models may skip adverse action notices
   • Creditors cannot avoid the specific-reasons requirement because their technology is too complex or opaque
   • Only banks, not fintechs, must explain complex models
3. A lender's model relies on bank-account cash-flow data, and the nearest sample form reason is only loosely related. What does Circular 2023-03 indicate?
   • The lender may use the nearest sample form reason
   • The lender must provide reasons that accurately describe the real factors, even if not on the sample form
   • The lender must stop using cash-flow data
   • The lender only needs to disclose the credit score
4. What does Regulation B's commentary say about disclosing many reasons?
   • At least ten reasons must always be listed
   • Disclosing more than four reasons is not likely to be helpful
   • Only one reason may ever be given
   • Reasons must be listed in alphabetical order
5. How do traditional scorecards typically generate adverse action reasons?
   • By comparing each attribute's points with the maximum possible points
   • By asking a loan officer to write reasons by hand
   • By using the applicant's income as the only reason
   • By ranking features alphabetically
6. Why does the choice of reference point matter when using SHAP values for adverse action reasons?
   • SHAP values are identical regardless of reference
   • Attributions are measured relative to a reference, so explaining against the average applicant can differ from explaining against one near the approval cutoff
   • The reference point must be the applicant with the highest score
   • References only matter for mortgage loans
7. Why do lenders group correlated features into reason families before ranking them?
   • To reduce the number of features the model uses
   • So attribution for one underlying factor is not diluted across several related variables
   • Because Regulation B lists required families
   • To hide which data sources the model uses
8. Which statement reflects the guide's view of explainability libraries?
   • Running any explainability library automatically produces compliant reasons
   • Attributions can be unstable and depend on choices, so reasons need validation for accuracy and clarity
   • Explainability tools are only needed for denied mortgage applications
   • Explainability tools also prove the model is fair