  This paper covers automated settlement of receivables in non-governmental
organizations. We tackle the problem with entity matching techniques. We
consider setup, where base algorithm is used for preliminary ranking of
matches, then we apply several novel methods to increase matching quality of
base algorithm: score post processing, cascade model and chain model. The
methods presented here contribute to automated settlement of receivables,
entity matching and multilabel classification in open-world scenario. We
evaluate our approach on real world operational data which come from company
providing settlement of receivables as a service: proposed methods boost recall
from 78% (base model) to >90% at precision 99%.
