On the problem of entity matching and its application in automated settlement of receivables
This work addresses automated settlement of receivables for non-governmental organizations, presenting an incremental improvement in entity matching techniques.
The paper tackles the problem of entity matching for automated settlement of receivables in non-governmental organizations, achieving a recall boost from 78% to over 90% at 99% precision using novel methods like score post-processing, cascade model, and chain model.
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%.