Developing an Application Scorecard using SAS

Explore model design parameters for scorecards, including development sample, historical and performance windows, and observation point, with examples of how window choices affect predictor and target variables.

Exclude fraudulent accounts, non score dependent loans (staff, VIPs, pre-approved), lost or stolen cards, underage or cancelled loans, and markets no longer operated from the development sample to reduce bias.

Explore a dataset of about 40,120 observations and learn how to handle coded purpose values, the data dictionary, and the default indicator for building an application scorecard in sas.

Identify historical observation window factors—enough data and seasonal influences like December—and define performance windows via vintage analysis for credit risk 12–24 months, 90+ DPD; telecom 6–12 months, 60+ DPD.

Develop an application scorecard for a personal loan portfolio by using logistic regression to predict default within one year based on at-time-of-application data and a 12-month performance period.

Analyze roll rate to define bad customers as those past due 90 days or more, using delinquency buckets and movement over 12 to 18 months to identify trends.

Explain why logistic regression suits scorecard development by modeling a binary outcome and estimating the probability of default, using log odds and the final score.

Import the APL dataset in SAS Studio, use proc contents to check variables and data types, then remove duplicates with proc sort and drop missing records to prepare clean scorecard.

Convert character variables to numeric with the SAS input function, create credit_amount and tenure_numb, drop originals, and use proc means to inspect descriptive stats and outliers for the data.

Learn to perform univariate analysis with proc univariate after calculating descriptive stats with proc means, assess normality and outliers, and cap age values at 62 in a loan dataset.

Use proc freq to analyze categorical variables, review frequency and percentage distributions, consult the data dictionary, and pursue resampling to achieve a uniformly represented, unbiased dataset.

Learn how information value selects predictive variables for binary logistic regression, guiding data hygiene, derived variable creation, and multicollinearity screening before model building.

Explore fine classing and coarse classing to capture non-linear relationships in logistic regression for credit risk modeling, grouping income and experience into categories, with Excel-based calculations.

Explore fine classing by splitting a continuous variable like income into deciles, then apply coarse classing to merge bins for a smoother weight-of-evidence and information-value-based scorecard.

Learn to calculate weight of evidence for bins, create the income_we variable, and use information value to screen predictors in SAS.

Explore how to perform bivariate analysis to identify predictive variables for scorecard development using SAS, including fine and coarse classing, information value, and weight of evidence.

Master coarse classing by binning variables with SAS using if-then-else to create age, income, and other bands, then convert bins to W variables for model development with IV-based selection.

Split data into development and validation samples using SAS (70/30), then generate and transform course class bins into woe variables and compute iv and woe values for model development.

Identify multicollinearity among independent variables using the variance inflation factor (VIF) in SAS proc reg with a threshold of two, and confirm no multicollinearity before building the logistic regression model.

Build a final SAS logistic regression model using a selected, significant set of predictors with good predictive strength, and generate betas and predicted probabilities for the default indicator.

Assess model efficacy by rank ordering the final logistic regression across deciles, then evaluate the Lawrence curve and Gini coefficient, and measure discriminatory power using AUC and the k statistic.

sorts the data by predicted probability to create ten equal bins and calculates rank ordering, ks statistics, gini coefficient, and related metrics for model evaluation.

Split data into development and validation sets, build the model on the development sample, and validate with rerun and scoring the validation sample to confirm sign stability and rank ordering.

Compute the Brier score by taking the mean of the squared differences between predicted probabilities and actual outcomes across bins; a lower score indicates better calibration.

Develop and validate a SAS logistic regression model using development and validation data sets, create dummy variables, run validation rerun, assess p-values and betas, and check rank ordering with deciles.

Apply reject inference to address selection bias in application scorecards by combining accepted and inferred rejects data to estimate unknown performance and build unbiased models.

Explore reject inference in scorecard development by addressing bias from missing rejects and compare methods like labeling all rejects as bad pairs and hard cutoff augmentation, noting the bad rate.

Explore reject inference methods, such as parcelling, hard cutoff augmentation, and fuzzy augmentation, by building an SAS scorecard on accepts to score and allocate rejects to good or bad.

Explore withdrawal inference as a sampling problem when no information exists for previously rejected customers, and learn how withdrawals affect scorecard development and possible uses of credit bureau data.