Developing Credit Risk Scorecard using Python

Define model design parameters by selecting historical and performance windows and observation point, using a development sample, to build a credit risk scorecard from good or bad outcomes and variables.

Exclude fraudulent accounts and loans based on non score dependent criteria from the development sample. Exclude underage, canceled or closed accounts, staff or VIP loans, lost cards, and non-operational markets.

Explore the credit risk scorecard dataset: 18,267 records across 23 variables, including debt burden ratio, dpd patterns, and demographics, to model default probability.

Choose the historical observation window with enough data, account for seasonality, and tailor it to the model type; use vintage analysis to define the performance window.

Predict default risk for existing personal loan customers using a behavioral scorecard built on past payment behavior, with logistic regression for a one-year horizon as an early warning tool.

Identify key retail predictors such as age, education, occupation, and income. Evaluate debt burden ratio, collateral, credit bureau status, delinquency, and last six months balance to forecast default.

Perform vintage (cohort) analysis to measure portfolio performance across opening months, determine a 24-month performance window, and track the cumulative bad rate by month on books with a line chart.

Roll rate analysis defines bad customers as delinquent 90 days or more, using the 0-29, 30-59, 60-89, and 90+ buckets, and shows 90+ days as the bad definition.

The lecture explains using logistic regression for binary scorecard development, modeling the probability of default with odds and log odds, yielding a score from estimated probability.

Import pandas and numpy to load the training data and inspect shape, info, and statistics. Clean missing values by replacing dots with nan to prepare the data for scorecard development.

Learn how information value screens variables for binary logistic regression, guiding data preparation, derived variables, and the prevention of multicollinearity in credit risk scorecard modeling.

Apply fine classing and coarse classing to group continuous and categorical variables, capture non-linear relationships, and strengthen credit risk scorecard models built with logistic regression, demonstrated in Excel.

Describe fine classing of a variable into decile bins with event, non-event percentages and weight of evidence, then use coarse classing to merge bins and smooth the curve, preserving continuity.

Explore how to compute the weight of evidence for each bin, create the income_w variable, and use information value ranges to screen variables for a credit risk scorecard model.

Perform fine classing in Python to compute w0 and IV for each variable, bin numeric data into deciles, and assess predictive power for credit risk scorecards.

Interpret fine classing outputs for credit risk scorecards by binning numeric vars into deciles and keeping categories, comparing IV and W values across bins to screen predictors.

Learn how coarse classing merges fine bins into stable groups, guided by monotonic default risk patterns and weight of evidence to simplify credit risk scorecard modeling.

Learn coarse classing of seven predictor variables into fewer bins based on iv values and default distribution, preparing for weight of evidence transformation and logistic regression in credit risk.

Transform binned categorical variables into woe values and compute information value to assess power for default. Map woe to features for logistic regression and multicollinearity checks to guide variable selection.

Check multicollinearity on woe-transformed numeric variables using VIF, compare information value with VIF, and decide which woe features to keep for a stable logistic regression model.

Builds a logistic regression scorecard using six woe variables after IV selection and multicollinearity checks, and interprets the model output with pseudo r square, log likelihood, and p values.

Assess the discriminatory power of a credit risk scorecard by examining concordant, discordant, and tied pairs, using estimated probabilities and scores to separate good and bad accounts.

Compute concordant, discordant and tied pairs from logistic regression outputs and interpret the concordance percentage to evaluate the model's ranking of defaulters above non-defaulters.

Learn how the c statistic (auc) and Somer's d measure a credit risk model's discrimination, compute them with roc_auc_score in Python, and interpret them for ranking events vs non-events.

Rank ordering validates credit risk models by showing higher predicted probabilities align with higher default rates across deciles, using mean scores and observed bad rates to confirm monotonic increases.

Understand Kolmogorov–Smirnov statistic and Gini coefficient as model discrimination checks after rank ordering. Learn how to compute and interpret them using the Lorenz curve and AUC.

Identify how accuracy and precision relate through a confusion matrix, compute TP, TN, FP, FN, and apply sklearn functions to assess credit risk scorecard performance.

Evaluate the Hosmer-Lemeshow goodness-of-fit test by comparing observed defaults to model-predicted defaults across deciles, and interpret the p-value to assess logistic regression fit.

Learn how to perform the clustering check in credit risk scorecards, ensuring scores differentiate accounts and keep clustering at each score within a 5–6% range for robust models.

Develop a credit risk scorecard by validating the logistic regression model through validation rerun and validation sample scoring, ensuring variable significance, stable coefficient signs, and rank ordering.

Learn how to evaluate credit risk probabilities with the Brier score, a calibration metric that measures the mean squared difference between the predicted probability of default and the actual outcome.