Towards a data-driven debt collection strategy based on an advanced machine learning framework

TL;DR

This paper presents a machine learning framework for debt collection that improves estimation of repayment likelihood, aiding strategy optimization and prioritization in the debt purchase industry.

Contribution

It introduces a novel ML pipeline with pre-processing and model selection that outperforms existing sector strategies using real debt industry data.

Findings

Outperforms current debt collection strategies

Validated with real historical debt data

Enhances estimation of repayment propensity

Abstract

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.