Probability of Default Models

Probability of default is one component of credit risk. In the Basel internal-ratings framework, PD is associated with a borrower grade and a defined default event; for corporate, sovereign and bank exposures, the reference horizon is one year. Other uses can define a different population or horizon, so a PD number is meaningful only when those choices are stated. It estimates the chance of default within the specified setup; it does not determine that a particular borrower will default. A model may rank borrowers by risk or produce estimates intended to align with observed default frequencies. Common approaches include logistic regression, survival methods and machine-learning models, but the algorithm alone does not define the target or validate the result. Data should match the use population as closely as possible, and features must be available at the point when the score is used. Defaults are relatively infrequent in many portfolios, so random splits can conceal time drift or leak information. Use time-aware validation where appropriate, compare to a simple benchmark, and check calibration as well as ranking performance. Banks using Basel IRB approaches face specific supervisory requirements for default definitions, data, estimation and validation. These are not universal requirements for every company that builds a credit score. Model risk guidance also emphasizes validation and limitations. State whether the output supports research, portfolio monitoring or lending decisions, and apply relevant consumer-protection, privacy and fair-lending rules. Do not describe PD as a guarantee or use a validation metric as a complete decision policy.