Towards a data-driven debt collection strategy based on an advanced
  machine learning framework
Abel Sancarlos Edgar Bahilo Pablo Mozo Lukas Norman Obaid Ur Rehma Mihails Anufrijevs
The European debt purchase market as measured by the total book value of
purchased debt approached 25bn euros in 2020 and it was growing at double-digit
rates. This is an example of how big the debt collection and debt purchase
industry has grown and the important impact it has in the financial sector.
However, in order to ensure an adequate return during the debt collection
process, a good estimation of the propensity to pay and/or the expected
cashflow is crucial. These estimations can be employed, for instance, to create
different strategies during the amicable collection to maximize quality
standards and revenues. And not only that, but also to prioritize the cases in
which a legal process is necessary when debtors are unreachable for an amicable
negotiation. This work offers a solution for these estimations. Specifically, a
new machine learning modelling pipeline is presented showing how outperforms
current strategies employed in the sector. The solution contains a
pre-processing pipeline and a model selector based on the best model
calibration. Performance is validated with real historical data of the debt
industry.