Developing Credit Risk Scorecard using SAS

Perform vintage analysis to determine the performance window, using cumulative bad rate versus months from account opening to identify a 24-month horizon.

Define the scorecard objective as predicting the one-year default probability for existing personal loan customers using a behavioral scorecard and logistic regression on key demographics, financial health, and payment behavior.

Explore the dataset for the credit risk scorecard in SAS, detailing 18,267 records and 23 variables grouped into demographic, financial, and payment behavior with a data dictionary and default flag.

Discover why logistic regression suits binary scorecards by modeling the probability of default, with log odds guiding score creation for default, attrition, and collection.

Inspect and clean data for credit risk scoring with SAS: use proc contents, means, freq, and univariate plots to identify missing values and outliers.

Explains how to assess variable predictive power with information value (IV) and weight of evidence (WOE) in SAS, select scorecard variables, and interpret IV ranges for model building.

Explain the Hosmer-Lemeshow test as a goodness-of-fit check for logistic models, using deciles of predicted scores to compare observed and expected good and bad outcomes in SAS.

Explore how concordant and discordant pairs, Somers D, and the C statistic evaluate a binary logistic model and its predicted probabilities in a SAS credit risk scorecard.

Rank a logistic regression model by deciles of predicted probabilities to confirm proper discrimination; analyze the Lorenz curve, Gini coefficient, and Kolmogorov-Smirnov statistic to gauge predictive strength.

Learn how to perform a clustering check in credit scorecards by transforming predicted probabilities into scores and evaluating score-point concentration with SAS frac, aiming for max clustering around 5–6%.

Assess model calibration using the brier score, the mean squared difference between predicted probabilities and actual outcomes, with examples and an Excel binning approach.