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

On the problem of entity matching and its application in automated settlement of receivables