credit-risk

To calculate credit risk metrics, you calibrate probability of default (PD), loss given default (LGD), and exposure at default (EAD) models. This Skill provides the framework to estimate these metrics and validate them against historical data and stress scenarios.

Loss aggregation in credit risk modeling is the process of aggregating expected and stressed losses at portfolio and desk levels. It combines individual obligor risk estimates to quantify total portfolio exposure and tail-loss risk.

Yes, you can perform tail-loss stress testing on credit portfolios. This Skill requires pandas and implements stress testing by validating calibrated risk metrics against predefined stress scenarios to measure portfolio resilience.

You validate credit risk models against historical data by running segment-aware calibration and validation checks. This Skill provides scripts to process input data and output validation results in JSON format for stable and explainable metrics.

Yes, this credit risk scoring approach supports production credit limit controls. It estimates obligor-level risk scores and aggregates losses to enforce risk controls across various organizational levels.