The European market for non-performing consumer loans is a well-defined institutional asset class, but pricing remains an exercise in inference under uncertainty. This paper sets out a practical, statistically grounded framework for using external credit-collection data and reference recovery curves to value consumer NPLs when internal data is sparse, unrepresentative or absent, drawing on the Global Credit Data representativeness guidelines, Bayesian-credibility blending theory and censored recovery-curve estimation.

The framework delivers a three-pillar methodology: rigorous representativeness assessment, censoring-aware recovery curves on EBA Template 5 cashflows, and a Bayesian-credibility blending. Worked examples demonstrate how credibility weights evolve as post-acquisition data accumulates, giving institutional investors a repeatable, auditable pricing discipline across cycles, sellers and product innovations.

Read the full article here: Valuation of Consumer NPL with External Data