A decision support system to optimize debt collection assignments

Sezi Cevik Onar; Basar Oztaysi; Cengiz Kahraman; Ersan Öztürk

doi:10.1007/978-3-030-23756-1_23

A decision support system to optimize debt collection assignments

Sezi Cevik Onar^*, Basar Oztaysi, Cengiz Kahraman, Ersan Öztürk

^*Corresponding author for this work

Department of Industrial Engineering

Turkcell Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Citations (Scopus)

Abstract

The technological developments let people use mobile phones and benefit from mobile phones in many areas of their lives. People benefit from various services of the operator companies. Therefore, operator companies have an extensive customer base. Yet, collecting the fees of their services from customers can be hard. When the customers regret or delay the payments the operator companies, which serve to millions of customers, face difficulties in legal procedures. The operator companies usually make agreements with the law firms to convey the lawsuits. In this study, a leading GSM operator company wants to know the possibility of finalizing the cases and take prevention on it, when transferring the case files to the law firms. Naive Bayes classifier, decision tree algorithms, k nearest neighbor method, support vector machines, random forest algorithm, and artificial neural network algorithms are examined, and Naive Bayes classification algorithm is used to define the collection difficulty level for the files.

Original language	English
Title of host publication	Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making - Proceedings of the INFUS 2019 Conference
Editors	Cengiz Kahraman, Sezi Cevik Onar, Basar Oztaysi, Irem Ucal Sari, Selcuk Cebi, A.Cagri Tolga
Publisher	Springer Verlag
Pages	178-187
Number of pages	10
ISBN (Print)	9783030237554
DOIs	https://doi.org/10.1007/978-3-030-23756-1_23
Publication status	Published - 2020
Event	International Conference on Intelligent and Fuzzy Systems, INFUS 2019 - Istanbul, Turkey Duration: 23 Jul 2019 → 25 Jul 2019

Publication series

Name	Advances in Intelligent Systems and Computing
Volume	1029
ISSN (Print)	2194-5357
ISSN (Electronic)	2194-5365

Conference

Conference	International Conference on Intelligent and Fuzzy Systems, INFUS 2019
Country/Territory	Turkey
City	Istanbul
Period	23/07/19 → 25/07/19

Bibliographical note

Publisher Copyright:
© 2020, Springer Nature Switzerland AG.

Keywords

Classification algorithms
Debt collection
GSM
Naïve Bayes

Access to Document

10.1007/978-3-030-23756-1_23

Cite this

Onar, S. C., Oztaysi, B., Kahraman, C., & Öztürk, E. (2020). A decision support system to optimize debt collection assignments. In C. Kahraman, S. Cevik Onar, B. Oztaysi, I. U. Sari, S. Cebi, & A. C. Tolga (Eds.), Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making - Proceedings of the INFUS 2019 Conference (pp. 178-187). (Advances in Intelligent Systems and Computing; Vol. 1029). Springer Verlag. https://doi.org/10.1007/978-3-030-23756-1_23

Onar, Sezi Cevik ; Oztaysi, Basar ; Kahraman, Cengiz et al. / A decision support system to optimize debt collection assignments. Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making - Proceedings of the INFUS 2019 Conference. editor / Cengiz Kahraman ; Sezi Cevik Onar ; Basar Oztaysi ; Irem Ucal Sari ; Selcuk Cebi ; A.Cagri Tolga. Springer Verlag, 2020. pp. 178-187 (Advances in Intelligent Systems and Computing).

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abstract = "The technological developments let people use mobile phones and benefit from mobile phones in many areas of their lives. People benefit from various services of the operator companies. Therefore, operator companies have an extensive customer base. Yet, collecting the fees of their services from customers can be hard. When the customers regret or delay the payments the operator companies, which serve to millions of customers, face difficulties in legal procedures. The operator companies usually make agreements with the law firms to convey the lawsuits. In this study, a leading GSM operator company wants to know the possibility of finalizing the cases and take prevention on it, when transferring the case files to the law firms. Naive Bayes classifier, decision tree algorithms, k nearest neighbor method, support vector machines, random forest algorithm, and artificial neural network algorithms are examined, and Naive Bayes classification algorithm is used to define the collection difficulty level for the files.",

keywords = "Classification algorithms, Debt collection, GSM, Na{\"i}ve Bayes",

author = "Onar, {Sezi Cevik} and Basar Oztaysi and Cengiz Kahraman and Ersan {\"O}zt{\"u}rk",

note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; International Conference on Intelligent and Fuzzy Systems, INFUS 2019 ; Conference date: 23-07-2019 Through 25-07-2019",

year = "2020",

doi = "10.1007/978-3-030-23756-1_23",

language = "English",

isbn = "9783030237554",

series = "Advances in Intelligent Systems and Computing",

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editor = "Cengiz Kahraman and {Cevik Onar}, Sezi and Basar Oztaysi and Sari, {Irem Ucal} and Selcuk Cebi and A.Cagri Tolga",

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Onar, SC , Oztaysi, B , Kahraman, C & Öztürk, E 2020, A decision support system to optimize debt collection assignments. in C Kahraman, S Cevik Onar, B Oztaysi, IU Sari, S Cebi & AC Tolga (eds), Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making - Proceedings of the INFUS 2019 Conference. Advances in Intelligent Systems and Computing, vol. 1029, Springer Verlag, pp. 178-187, International Conference on Intelligent and Fuzzy Systems, INFUS 2019, Istanbul, Turkey, 23/07/19. https://doi.org/10.1007/978-3-030-23756-1_23

A decision support system to optimize debt collection assignments. / Onar, Sezi Cevik ; Oztaysi, Basar ; Kahraman, Cengiz et al.
Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making - Proceedings of the INFUS 2019 Conference. ed. / Cengiz Kahraman; Sezi Cevik Onar; Basar Oztaysi; Irem Ucal Sari; Selcuk Cebi; A.Cagri Tolga. Springer Verlag, 2020. p. 178-187 (Advances in Intelligent Systems and Computing; Vol. 1029).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - A decision support system to optimize debt collection assignments

AU - Onar, Sezi Cevik

AU - Oztaysi, Basar

AU - Kahraman, Cengiz

AU - Öztürk, Ersan

PY - 2020

Y1 - 2020

N2 - The technological developments let people use mobile phones and benefit from mobile phones in many areas of their lives. People benefit from various services of the operator companies. Therefore, operator companies have an extensive customer base. Yet, collecting the fees of their services from customers can be hard. When the customers regret or delay the payments the operator companies, which serve to millions of customers, face difficulties in legal procedures. The operator companies usually make agreements with the law firms to convey the lawsuits. In this study, a leading GSM operator company wants to know the possibility of finalizing the cases and take prevention on it, when transferring the case files to the law firms. Naive Bayes classifier, decision tree algorithms, k nearest neighbor method, support vector machines, random forest algorithm, and artificial neural network algorithms are examined, and Naive Bayes classification algorithm is used to define the collection difficulty level for the files.

AB - The technological developments let people use mobile phones and benefit from mobile phones in many areas of their lives. People benefit from various services of the operator companies. Therefore, operator companies have an extensive customer base. Yet, collecting the fees of their services from customers can be hard. When the customers regret or delay the payments the operator companies, which serve to millions of customers, face difficulties in legal procedures. The operator companies usually make agreements with the law firms to convey the lawsuits. In this study, a leading GSM operator company wants to know the possibility of finalizing the cases and take prevention on it, when transferring the case files to the law firms. Naive Bayes classifier, decision tree algorithms, k nearest neighbor method, support vector machines, random forest algorithm, and artificial neural network algorithms are examined, and Naive Bayes classification algorithm is used to define the collection difficulty level for the files.

KW - Classification algorithms

KW - Debt collection

KW - GSM

KW - Naïve Bayes

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DO - 10.1007/978-3-030-23756-1_23

M3 - Conference contribution

AN - SCOPUS:85069518149

SN - 9783030237554

T3 - Advances in Intelligent Systems and Computing

SP - 178

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BT - Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making - Proceedings of the INFUS 2019 Conference

A2 - Kahraman, Cengiz

A2 - Cevik Onar, Sezi

A2 - Oztaysi, Basar

A2 - Sari, Irem Ucal

A2 - Cebi, Selcuk

A2 - Tolga, A.Cagri

PB - Springer Verlag

T2 - International Conference on Intelligent and Fuzzy Systems, INFUS 2019

Y2 - 23 July 2019 through 25 July 2019

ER -

Onar SC , Oztaysi B , Kahraman C, Öztürk E. A decision support system to optimize debt collection assignments. In Kahraman C, Cevik Onar S, Oztaysi B, Sari IU, Cebi S, Tolga AC, editors, Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making - Proceedings of the INFUS 2019 Conference. Springer Verlag. 2020. p. 178-187. (Advances in Intelligent Systems and Computing). doi: 10.1007/978-3-030-23756-1_23

A decision support system to optimize debt collection assignments

Abstract

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Bibliographical note

Keywords

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