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

A retail bank is building an application scorecard for unsecured personal loans. The development team groups a continuous variable, 'months at current address', into several bins and assigns each bin a weight of evidence (WoE). What is the primary purpose of computing WoE for each bin?

Weight of evidence expresses, for each bin, the log of the distribution of goods divided by the distribution of bads. This gives a log-odds based, comparable and usually monotonic input for logistic regression scorecards. It is not a loss severity measure, a binning rule or a regulatory PD calibration.

  1. ATo measure the log odds of good to bad in the bin relative to the overall population, giving a monotonic, comparable input for logistic regressionCorrect
  2. BTo estimate the loss given default for borrowers falling in each bin
  3. CTo adjust the bin boundaries so that each bin holds exactly the same number of applicants
  4. DTo convert the scorecard score into a through-the-cycle probability of default for regulatory capital

Explanation

WoE is the natural log of the ratio of the bin's share of goods to its share of bads. It puts each attribute on a log-odds scale that suits logistic regression and highlights whether the variable's relationship to risk is monotonic. It is not an LGD estimate, a binning rule, or a PD calibration.

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