Modelling Recovery Rates in Mass Debt Portfolios Using Machine Learning Algorithms

The growing use of data analytics in the debt collection sector has increased the demand for accurate and transparent models capable of predicting recovery levels in large portfolios of mass receivables. Despite the rising importance of machine-learning methods in financial analysis, the literature still lacks empirical studies based on real operational data from large-scale portfolios. This work addresses this gap by conducting a comparative assessment of three tree-based machine-learning algorithms: decision tree, random forest and XGBoost. The models were trained on ex-ante data derived from 389,250 actual receivables serviced by an entity operating on the Polish debt collection market.