Forecasting Recovery Rates on Non-Performing Loans with Machine Learning

Abstract

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.

Keywords: Risk Management, Recovery Rate, Non-Performing Loans, Forecasting

JEL Classification: G21, G23, C52