Predicting Performance of Legal Debt Collection Agency

Nilüfer Altınok*, Elmira Farrokhizadeh, Ahmet Tekin, Sara Ghazanfari Khameneh, Basar Oztaysi, Sezi Çevik Onar, Özgür Kabak, Ali Kasap, Aykut Şahin, Mehmet Ayaz

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Özet

In this competitive world, companies need to manage their arrears of debtors to survive. Big companies monthly face thousands of debt cases from their customers and if these debtor customers do not pay their debts within the specified period, these cases will enter the legal stage as legal debt collection that is handled by experienced lawyers. At the legal stage, the cases are sent to the contracted legal debt collection agencies to start a lawsuit. Therefore, assigning which case to which legal debt collection agency is a significant and critical issue in the company’s success in getting its debts in the legal stage. So, this study aims to find the legal debt collection agency, which has more capability to close the assigned debt case by predicting case closing probability with machine learning techniques based on past historical data. To predict case closing probability, 8 machine learning algorithms, Catboost Classifier, Extreme Gradient Boosting Classifier, Gradient Boosting Classifier etc., are applied to the processed dataset. The results show us Catboost Classifier has the best accuracy performance 0.87 accuracy. Also, the results show us boosting type ensemble learning algorithms have better performance than other algorithms. Finally, we tune hyper-parameters of Catboost classifier to get better accuracy in the modeling and applied k-fold cross-validation for testing the model’s testing stability.

Orijinal dilİngilizce
Ana bilgisayar yayını başlığıIntelligent and Fuzzy Techniques for Emerging Conditions and Digital Transformation - Proceedings of the INFUS 2021 Conference
EditörlerCengiz Kahraman, Selcuk Cebi, Sezi Cevik Onar, Basar Oztaysi, A. Cagri Tolga, Irem Ucal Sari
YayınlayanSpringer Science and Business Media Deutschland GmbH
Sayfalar514-522
Sayfa sayısı9
ISBN (Basılı)9783030855765
DOI'lar
Yayın durumuYayınlandı - 2022
EtkinlikInternational Conference on Intelligent and Fuzzy Systems, INFUS 2021 - Istanbul, Turkey
Süre: 24 Ağu 202126 Ağu 2021

Yayın serisi

AdıLecture Notes in Networks and Systems
Hacim308
ISSN (Basılı)2367-3370
ISSN (Elektronik)2367-3389

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???event.eventtypes.event.conference???International Conference on Intelligent and Fuzzy Systems, INFUS 2021
Ülke/BölgeTurkey
ŞehirIstanbul
Periyot24/08/2126/08/21

Bibliyografik not

Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Finansman

Acknowledgement. This work is supported by TUBITAK (The Scientific and Technological Research Council of Turkey, Project Id: 5200012).

FinansörlerFinansör numarası
TUBITAK
Türkiye Bilimsel ve Teknolojik Araştirma Kurumu5200012

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