Developing Credit Risk Scorecard using Python

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

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.

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.

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.

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.