A fair level of provisions on bad and doubtful loans is an essential input in mark-to-market accounting, and in the calculation of bank profitability, capital and solvency. Loan-loss provisioning is directly related to estimates of loan-loss given default (LGD). A literature on LGD on bank loans is developing but, surprisingly, it has not been exploited to address, at the micro level, the issue of provisioning at the time of default, and after the default date. For example, in Portugal, the central bank imposes a mandatory provisioning schedule based on the time period since a loan is declared ‘non-performing’. The dynamic schedule is ‘ad hoc’, not based on empirical studies. The purpose of the paper is to present an empirical methodology to calculate a fair level of loan-loss provisions, at the time of default and after the default date. To illustrate, a dynamic provisioning schedule is estimated with micro-data provided by a Portuguese bank on recoveries on non-performing loans. This schedule is then compared to the regulatory provisioning schedule imposed by the central bank.

The empirical literature on credit risk has relied mostly on the corporate bond market to estimate losses in the event of default. The reason for this is that, as bank loans are private instruments, few data on loan losses are publicly available. The contribution of this paper is to apply mortality analysis to a unique set of micro-data on defaulted bank loans of a European bank. The empirical results relate to the timing of recoveries on bad and doubtful bank loans, the distribution of cumulative recovery rates, their economic determinants and the direct costs incurred by that bank on recoveries on bad and doubtful loans.