Skip to content

FRM Part II · FRM Exam Part II · Credit Scoring and Retail Credit Risk Management

A lender builds a scorecard using only approved applicants who were later observed to repay or default. Rejected applicants have no performance data. Which technique is designed to address the resulting sample selection bias?

Reject inference is the technique used. It estimates likely good or bad outcomes for rejected applicants so the scorecard is developed on a sample representing all applicants, not just those previously approved, reducing sample selection bias.

  1. AReject inferenceCorrect
  2. BWeight of evidence binning
  3. CPopulation stability index monitoring
  4. DOut-of-time validation

Explanation

Reject inference assigns or estimates likely outcomes for rejected applicants so the model reflects the full through-the-door population. WoE binning transforms variables, PSI tracks population drift after deployment, and out-of-time validation tests stability over time. None corrects selection bias from missing rejected-applicant outcomes.

Did you get it right without looking?

One question tells you little. A timed set on Credit Scoring and Retail Credit Risk Management shows your real accuracy, how long you take and where you lose marks.

Practise Credit Scoring and Retail Credit Risk ManagementFree diagnostic mock

More Credit Scoring and Retail Credit Risk Management questions