CCP Credit Scoring & Probability of Default 2

Question 1

Which statistical technique is most commonly used to build logistic regression-based credit scorecards?

Accepted Answer

Binary logistic regression

Answer

Binary logistic regression models the probability of a binary outcome (default/non-default), making it the standard method for scorecard development.

Question 2

A credit model's Gini coefficient is 0.72. How does this compare to a random model?

Accepted Answer

It significantly outperforms a random model, which has a Gini of 0

Answer

A random model has a Gini of 0, so a Gini of 0.72 indicates strong discriminatory power well above random chance.

Question 3

What does the term 'scorecard points-to-double-the-odds' (PDO) represent?

Accepted Answer

The number of score points needed to double the odds of being a good credit risk

Answer

PDO defines the score increment required to double the odds ratio of good-to-bad, calibrating the scorecard's scaling.

Question 4

Which variable transformation is typically applied to predictors before building a logistic scorecard to measure predictive power?

Accepted Answer

Weight of Evidence (WoE) transformation

Answer

WoE transformation converts categorical or binned continuous variables into log-odds format, enabling straightforward use in logistic regression scorecards.

Question 5

A lender observes that its scorecard's KS statistic has dropped from 42 to 28 over two years. What does this most likely indicate?

Accepted Answer

Model drift — the scorecard no longer discriminates defaults as well

Answer

A declining KS statistic signals model drift, meaning the scorecard has lost predictive power, likely due to population or economic shifts.

(CCP) Certified Credit Professional Practice Test

(CCP) Certified Credit Professional Practice Test

CCP Credit Scoring & Probability of Default 2