Managing Credit Risk for Retail Low-Default Portfolios

Abstract

Low-Default Portfolios (LDPs) form a significant and substantial portion of retail assets at major financial institutions. However, in the literature, there are few contributions that deal specifically with the problem of managing LDP credit risk for retail portfolios. My goal is to analyze a possible realistic methodology to develop scoring or rating systems for the retail portfolios where the number of defaults is low or equal to zero. I find that generic scoring models (expert scorecards based on subjective weights or developed on pooled data) are neither the only way nor the most accurate to solve the problem of modeling retail credit risk in conditions of relatively sparse empirical default data. I demonstrate that the proposed methodology is a better alternative than generic models to manage retail LDP credit risk and has a performance close to estimations based on sufficient and meaningful internal data. An associated objective is to show that the proposed technique can be used to facilitate risk assessment in the absence of sufficient historical default data also in the Basel II context.