An Algorithmic Model for Retail Credit Portfolio Segmentation

Under the new Basel bank capital framework, each bank must group its retail exposures into multiple segments with homogeneous risk characteristics. The U.S. regulatory agencies believe that each bank may use its internal risk models for the loan-level risk parameter estimates such as probability of default (PD) and loss given default (LGD) to group individual exposures into the resultant segments with homogeneous risk attributes. In stark contrast to the conventional decision-tree method, we propose a new algorithmic technique for retail consumer loan portfolio segmentation. This new technique identifies the optimal number of segments, sorts the individual loan exposures into the various segments, and then leads to the minimal degree of risk heterogeneity in comparison to the baseline equal-bin and quantile-bin schemes. Furthermore, we analyze the Monte Carlo implicit asset correlation values for the retail loan segments over time to help assess the implications for bank capital measurement. The best-fit method for retail credit portfolio segmentation results in some capital relief that serves as an economic incentive for the bank to invest in this alternative segmentation. This positive outcome accords with the core principle of statistical conservatism that the financial econometrician enshrines in the Basel regulatory requirements for bank capital measurement