  Benchmarks are standards that allow to identify opportunities for improvement
among comparable units. This study suggests a 2-step methodology for
calculating probabilistic benchmarks in noisy data sets: (i) double-hyperbolic
undersampling filters the noise of key performance indicators (KPIs), and (ii)
a relevance vector machine estimates probabilistic benchmarks with denoised
KPIs. The usefulness of the methods is illustrated with an application to a
database of nano-finance+. The results indicate that-in the case of
nano-finance groups-a higher discrimination power is obtained with variables
that capture the macro-economic environment of the country where a group
operates. Also, the estimates show that groups operating in rural regions have
different probabilistic benchmarks, compared to groups in urban and peri-urban
areas.
