We compare the performances of a wide set of regression techniques and machine
learning algorithms for predicting recovery rates on non-performing loans, using a private database from a European debt collection agency. We find that rule-based algorithms
such as Cubist, boosted trees and random forests perform significantly better than other
approaches. In addition to loan contract specificities, the predictors referring to the bank
recovery process – prior to the portfolio’s sale to the debt collector – are also proven to
strongly enhance forecasting performances. These variables, derived from the time-series of
contacts to defaulted clients and clients’ reimbursements to the bank, help all algorithms to
better identify debtors with different repayment ability and/or commitment, and in general
with different recovery potential.