Forecasting recovery rates on non-performing loans with machine learning
(2020) International Journal of Forecasting — Vol. 37, n° 1, p. 428-444 (2020)
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- Authors
Gambetti, Paolo 
- 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.
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Citations
Bellotti, A., Brigo, D., Gambetti, P., & Vrins, F. (2020). Forecasting recovery rates on non-performing loans with machine learning. International Journal of Forecasting, 37(1), 428-444. https://hdl.handle.net/2078.5/268911 (Original work published 2020)