Evaluation of legal debt collection services by using Hesitant Pythagorean (Intuitionistic Type 2) fuzzy AHP

Çevik Onar S., Oztaysi B., Kahraman C., Ozturk E.

JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, vol.38, no.1, pp.883-894, 2020 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 38 Issue: 1
  • Publication Date: 2020
  • Doi Number: 10.3233/jifs-179456
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Applied Science & Technology Source, Business Source Elite, Business Source Premier, Communication Abstracts, Compendex, Computer & Applied Sciences, INSPEC, Metadex, zbMATH, Civil Engineering Abstracts
  • Page Numbers: pp.883-894
  • Istanbul Technical University Affiliated: Yes


Managing the collection of unpaid debts is crucial for the financial survival of the companies. The long term unpaid debts are collected through legal debt collection processes. This legal process should be carried out by qualified lawyers. The companies with many subscribers usually work with legal debt collection offices outside the company rather than allocate internal resources for the management of this process. Evaluating the performances of legal debt collection offices and appropriate distribution of the relevant debtor files to different legal debt collection offices located in different regions are very important for optimizing the debt collection. One of the biggest GSM operators in Turkey that has millions of customers wants to enhance its legal debt collection process. Due to the high number of customer, the GSM operator works approximately one hundred legal debt collection offices which makes evaluation complex. This complex evaluation process should be objective, transparent, and represent the company vision and strategy. The legal debt collection offices should not only increase the total amount of collected debts but also avoid creating compliance problems and customer dissatisfaction. The evaluation of legal debt collection offices should involve both of these objective and subjective criteria. Yet, the evaluations involve hesitancy and vagueness. In this study, we use hesitant Pythagorean fuzzy sets for evaluating the performances of the legal debt collection offices and apply it the real data.