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A fuzzy rule based inference system for early debt collection

    Sezi Cevik Onar Affiliation
    ; Basar Oztaysi Affiliation
    ; Cengiz Kahraman Affiliation

Abstract

Nowadays, unpaid invoices and unpaid credits are becoming more and more common. Large amounts of data regarding these debts are collected and stored by debt collection agencies. Early debt collection processes aim at collecting payments from creditors or debtors before the legal procedure starts. In order to be successful and be able to collect maximum debts, collection agencies need to use their human resources efficiently and communicate with the customers via the most convenient channel that leads to minimum costs. However, achieving these goals need processing, analyzing and evaluating customer data and inferring the right actions instantaneously. In this study, fuzzy inference based intelligent systems are used to empower early debt collection processes using the principles of data science. In the paper, an early debt collection system composed of three different Fuzzy Inference Systems (FIS), one for credit debts, one for credit card debts, and one for invoices, is developed. These systems use different inputs such as amount of loan, wealth of debtor, part history of debtor, amount of other debts, active customer since, credit limit, and criticality to determine the output possibility of repaying the debt. This output is later used to determine the most convenient communication channel and communication activity profile.

Keyword : fuzzy inference system, early debt collection, credit, credit card, overdraft, invoice

How to Cite
Onar, S. C., Oztaysi, B., & Kahraman, C. (2018). A fuzzy rule based inference system for early debt collection. Technological and Economic Development of Economy, 24(5), 1845-1865. https://doi.org/10.3846/20294913.2016.1266409
Published in Issue
Oct 1, 2018
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This work is licensed under a Creative Commons Attribution 4.0 International License.

References

Abe, N.; Thomas, V. P.; Kowalczyk, M.; Melville, P.; Pendus, C.; Reddy, C. K.; Jensen, D. L.; Bennett, J. J.; Anderson, G. F.; Cooley, B. R.; Domick, M.; Gardinier, T. 2010. Optimizing debt collections using constrained reinforcement learning, in ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, July 25–28, Washington, DC, USA, 75–84. https://doi.org/10.1145/1835804.1835817

Behret, H.; Oztaysi, B.; Kahraman, C. 2011. A fuzzy inference system for supply chain risk management, in Y. Wang, T. Li (Eds.). Practical applications of intelligent systems. Berlin, Heidelberg: Springer, 429–438.

Boyacioglu, M. A.; Avci, D. 2010. An Adaptive Network-Based Fuzzy Inference System (ANFIS) for the prediction of stock market return: the case of the Istanbul Stock Exchange, Expert Systems with Applications 37(12): 7908–7912. https://doi.org/10.1016/j.eswa.2010.04.045

Cevik Onar, S.; Ates, N. Y. 2008. A fuzzy model for operational supply chain optimization problems, Multiple-Valued Logic and Soft Computing 14(3–5): 355–370.

Cevik Onar, S.; Oztaysi, B.; Otay, İ.; Kahraman, C. 2015. Multi-expert wind energy technology selection using interval-valued intuitionistic fuzzy sets, Energy 90(1): 274–285. https://doi.org/10.1016/j.energy.2015.06.086

Chen, S. C.; Huang, M. Y. 2011. Constructing credit auditing and control & management model with data mining technique, Expert Systems with Applications 38(5): 5359–5365. https://doi.org/10.1016/j.eswa.2010.10.020

Chin, A. G.; Kotak, H. 2006. Improving debt collection processes using rule-based decision engines: a case study of Capital One, International Journal of Information Management 26(1): 81–88. https://doi.org/10.1016/j.ijinfomgt.2005.10.002

FDSPA. 2014. Fair Debt Collection Practices Act [online], [cited 20 May 2015] Available from Internet: http://files.consumerfinance.gov/f/201403_cfpb_fair-debt-collection-practices-act.pdf

Fei, C. I. 2010. Evaluate the performance of cardholders’ repayment behaviors using artificial neural networks and data envelopment analysis, in Sixth International Conference on Networked Computing and Advanced Information Management (NCM), 16–18 August 2010, Seoul, Korea, 478–483.

Georgopoulos, E. F.; Giannaropoulos, S. M. 2007. Solving resource management optimization problems in contact centers with artificial neural networks, in 19th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2007), 29–31 October 2007, Patras, Greece, 405–412.

Giovanis, E. 2012. Study of discrete choice models and adaptive neuro-fuzzy inference system in the prediction of economic crisis periods in USA, Economic Analysis and Policy 42(1): 79–95. https://doi.org/10.1016/S0313-5926(12)50006-8

Hector, C. 2011. Debt collection in the information age: new technologies and the fair debt collection practices act, California Law Review 99(6): 1601.

Howard, E. 2012. Survey forecasts debt collection crisis: big issues and increase in debt cases – and no clear remedy, UK Business Today 33(7/8): 667.

Huls, N. 1992. American influences on European consumer bankruptcy law, Journal of Consumer Policy 15: 125–142. https://doi.org/10.1007/BF01352132

IBISworld. 2014. Debt collection agencies market research report [online], [cited 16 May 2015]. Available from Internet: http://www.ibisworld.com/industry/default.aspx?indid=1474

Jee, T. L.; Tay, K. M.; Lim, C. P. 2015. A new two-stage fuzzy inference system-based approach to prioritize failures in failure mode and effect analysis, IEEE Transaction on Reliability 64(3): 869–877. https://doi.org/10.1109/TR.2015.2420300

Jones, P. M.; Roy, R.; Corbett, J. 2004. Fuzzy Information, in Processing of IEEE Annual Meeting of the NAFIPS’04, 533–538.

Kahraman, C.; Beskese, A.; Kaya, I. 2010. Selection among ERP outsourcing alternatives using a fuzzy multi-criteria decision making methodology, International Journal of Production Research 48(2): 547–566. https://doi.org/10.1080/00207540903175095

Kaya, I.; Oztaysi, B.; Kahraman, C. 2012. A two-phased fuzzy multicriteria selection among public transportation investments for policy-making and risk governance, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 20: 31–48. https://doi.org/10.1142/S021848851240003X

Lund, S. 2010. Soft debt collection – collecting money without alienating your customer, in J. Reuvid (Ed.). The Business guide to credit management: advice and solutions for cost control, financial risk management and capital protection. Kogan Page, Ltd., 119–125.

Oztaysi, B.; Behret, H.; Kabak, O.; Uçal Sarı, I.; Kahraman, C. 2013. Fuzzy inference systems for disaster response, in B. Vitoriano, J. Montero, D. Ruan (Eds.). Decision aid models for disaster management and emergencies. Paris: Atlantis Pres, 75–94.

Rezvan, P.; Azadnia, A. H.; Noordin, M. Y.; Seyedi, S. N. 2014. Sustainability assessment methodology for concrete manufacturing process: a fuzzy inference system approach, Advanced Materials Research 845: 814–818.

Ross, T. J. 1995. Fuzzy logic with engineering applications. Addison Wesley.

Sangaiah, A. K.; Thangavelu, A. K.; Gao, X. Z.; Anbazhagan, N.; Durai, M. S. 2015. An ANFIS approach for evaluation of team-level service climate in GSD projects using Taguchi-genetic learning algorithm, Applied Soft Computing 30: 628–635. https://doi.org/10.1016/j.asoc.2015.02.019

Shoorehdeli, M. A.; Teshnehlab, M.; Sedigh, A. K. 2009. Training ANFIS as an identifier with intelligent hybrid stable learning algorithm based on particle swarm optimization and extended Kalman filter, Fuzzy Sets and Systems 160(7): 922–948. https://doi.org/10.1016/j.fss.2008.09.011

Takahashi, M.; Tsuda, K. 2013. Towards early detections of the bad debt customers among the mail order industry, in T. Matsuo, R. C. Palacios (Eds.). Electronic business and marketing: new trends on its process and applications, 167–176.

Tan, P. N.; Steinbach, M.; Kumar, V. 2006. Introduction to data mining. Boston, USA: Pearson International Edition.

Tavana, M.; Azizi, F.; Azizi, F.; Behzadian, M. 2013. A fuzzy inference system with application to player selection and team formation in multi-player sports, Sport Management Review 16(1): 97–110. https://doi.org/10.1016/j.smr.2012.06.002

Tay, K. M.; Lim, C. P. 2008a. On the use of fuzzy inference techniques in assessment models. Part I – Theoretical Properties, Fuzzy Optimization and Decision Making 7(3): 269–281.

Tay, K. M.; Lim, C. P. 2008b. On the use of fuzzy inference techniques in assessment models. Part II – Industrial applications, Fuzzy Optimization and Decision Making 7(3): 283–302.

Vecchio, M. D.; Jin, S.; Mistretta, A.; Rolando, H.; Tuck, H. 2006. Designing a search mechanism for debt collection in Systems and Information Engineering Design Symposium IEEE, April 26–28, Charlottesville, VA, 168–173.

Wang, H. Y.; Liao, C.; Kao, C. H. 2013. A credit assessment mechanism for wireless telecommunication debt collection: an empirical study, Information Systems and e-Business Management 11(3): 357–375. https://doi.org/10.1007/s10257-012-0192-x

Yaici, W.; Entchev, E. 2016. Adaptive Neuro-Fuzzy Inference System modelling for performance prediction of solar thermal energy system, Renewable Energy 86: 302–315. https://doi.org/10.1016/j.renene.2015.08.028