How to Define the Modeling Scope of an Internal Credit Risk Model

Building an internal credit risk model starts with a clear, regulated definition of what constitutes a default. To manage risk, large, diverse portfolios are segmented into smaller, homogeneous groups using filters like company revenue or loan type. The modeling dataset is constructed by observing healthy counterparties at the end of one year (N) and using their characteristics to predict whether they will default in the following year (N+1). This process is repeated over several years of historical data, typically at least five, to capture different economic cycles and ensure enough default events for reliable model training. This structured approach to data preparation is essential for reducing model risk and ensuring the model is representative of the portfolio it is intended for.