Credit risk is an important aspect in the activity of commercial banks. Regulators require from banks to measure credit risk within Basel II and also during the Internal Capital Assessment and Adequacy Process (ICAAP) and stress tests. Name concentration in a lending portfolio arises when there are few borrowers in a bank portfolio or when loan amounts are very unequal in distribution. The portfolio credit risk model underpinning the Basel II Internal Ratings-Based (IRB) approach does not account for name concentration. To measure the latter the literature proposes specific concentration indexes such as the Herfindahl-Hirschman index, the Gini index or more general approaches like the granularity adjustment (GA) to calculate the appropriate economic capital needed to cover the risk arising from the potential default of large borrowers. This paper investigates the practical aspects of granularity adjustment, Gini index and Herfindahl index for contribution’s quantification of name concentrations to portfolio risk. We try also to extend the upper bound approach of GA developed by Gordy and Lütkebohmert (2007). For many banks, this approach would permit dramatic reductions in data requirements relative to the full GA.