How to Study the Monotonicity and Stability of Variables in a Scoring Model using Python

Building a robust credit scoring model requires analyzing variable monotonicity and stability before training. Monotonicity ensures that a variable's relationship with risk is consistent, such as higher income always corresponding to lower default rates. Stability analysis verifies that this risk direction holds true over time and that the variable's distribution does not shift significantly between datasets. The author demonstrates these concepts by discretizing variables, plotting default rates over time, and introducing the Population Stability Index (PSI) to measure distributional shifts. This process leads to the exclusion or modification of variables that do not exhibit stable, monotonic behavior, ensuring a more reliable model in production.