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

Autor:	Czekaj, Lukasz, Biegus, Tomasz, Kitlowski, Robert, Raczynski, Stanislaw, Olszewski, Mateusz, Dziedzic, Jakub, Tomasik, Paweł, Kozera, Ryszard, Prokopenya, Alexander, Olszewski, Robert
Rok vydání:	2022
Předmět:	Computer Science - Machine Learning
Zdroj:	In: Modelling and Simulation 2021. The European Simulation and Modelling Conference 2021 / Armenia Stefano, Geril Philippe (eds.), 2021, EUROSIS-ETI, pp.37-41, ISBN 978-9-492-85918-1
Druh dokumentu:	Working Paper
Popis:	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%. Comment: 5 pages, 5 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2205.10678 Zobrazit plný text záznamu View this record from Arxiv