Abstract We compare the performance of a wide set regression techniques and machine-learning algorithms for predicting recovery rates on non-performing loans, using private database from European debt collection agency. find that rule-based such as Cubist, boosted trees, random forests perform significantly better than other approaches. In addition to loan contract specificities, predictors ref...