Bayesian clustering for portfolio credit risk

Bayesian clustering forms data-driven borrower risk buckets.Weighted memberships capture diversified borrower exposures.Improves loss, VaR, and ES estimates vs. fixed buckets.Quantifies uncertainty in PDs, correlations, and cluster weights. Credit risk models for loan portfolios typically assume that exposures can be assigned to homogeneous risk buckets, as in Vasicek- and Basel-style portfolio credit risk models.We propose a Bayesian clustering model for constructing homogeneous risk buckets directly from loan credit histories. In contrast to traditional segmentation, the framework assigns weighted memberships across multiple clusters, capturing cross-sector and multifactor exposures more realistically. Using both simulated and real credit data, we find that the proposed method can improve the estimation of loss distributions, value-at-risk and expected shortfall. The approach provides a statistically robust and operationally tractable alternative to conventional bucketing, offering a more flexible foundation for portfolio credit risk management and capital assessment. Copyright Infopro Digital Limited. All rights reserved. You may share this content using our article tools. As outlined in our terms and conditions, https://www.infopro-digital.com/terms-and-conditions/subscriptions/ (clause 2.4), an Authorised User may only make one copy of the materials for their own personal use. You must also comply with the restrictions in clause 2.5. If you would like to purchase additional rights please email info@risk.net