  This article proposes a method for measuring the latent risks involved in the
recovery process of non performing loans in financial institutions and business
firms that deal with collection and recovery processes. To that end, we apply
the competing risks model referred to in the literature as the promotion time
model. The result achieved is the probability of credit recovery for a
portfolio segmented into groups based on the information available. Within the
context of competing risks, application of the technique yielded an estimation
of the number of latent events that concur to the credit recovery event. With
these results in hand, we were able to compare groups of defaulters in terms of
risk or susceptibility to the recovery event during the collection process, and
thereby determine where collection actions are most efficient. We specify the
Poisson distribution for the number of latent causes leading to recovery, and
the Weibull distribution for the time up to recovery. To estimate the model
parameters, we use the maximum likelihood method. Finally, the model was
applied to a sample of defaulted loans from a financial institution.
