How to Define the Modeling Scope of an Internal Credit Risk Model | Towards Data Science

Towards Data Science

by JUNIOR JUMBONG

February 25, 2026

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The article explores the critical process of defining the modeling scope for internal credit risk models, particularly focusing on Probability of Default (PD) models. As the banking industry undergoes significant transformation driven by technological advancements, understanding and managing credit risk has become more complex. The European Central Bank (ECB) permits banks to use internal models to assess credit risk across various exposures, such as corporate loans or real estate financing. These models estimate key parameters like PD, Exposure at Default (EAD), and Loss Given Default (LGD), with a primary emphasis on PD models for assigning borrower ratings and calculating regulatory capital requirements. The article highlights the importance of clearly defining default to accurately assess credit risk. A standardized definition of default was established post the 2008 financial crisis, incorporating criteria such as significant financial deterioration, payment arrears, forbearance situations, and contagion effects. For instance, a counterparty is considered in default if they have material payment arrears exceeding 90 days. This uniform approach ensures consistency across banking institutions. To enhance risk management, banks must segment their portfolios into homogeneous sub-portfolios, such as large corporations, SMEs, retail clients, or sovereign entities. This segmentation allows for more accurate modeling by tailoring approaches to specific client categories. The article also discusses the use of filters—variables like revenue thresholds—to identify and retain counterparties that fit the desired scope for analysis. Defining the modeling scope is essential for constructing reliable credit risk models. By clearly defining default, segmenting portfolios, and applying appropriate filters, institutions can build more accurate PD models. This process not only aids in regulatory compliance but also contributes to effective capital management by ensuring banks are well-prepared to handle unexpected losses. For data professionals and AI enthusiasts, understanding these modeling techniques is crucial as they highlight the growing role of data science in financial risk management.

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Originally published on Towards Data Science on 2/25/2026