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A credit assessment mechanism for wireless telecommunication debt collection: an empirical study

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Abstract

The wireless telecommunication market in Taiwan is now saturated. As the competition between wireless service carriers intensifies, retaining customers becomes more difficult. To prevent companies from increasing bad debt and experiencing customer churn, this study aims to apply data mining method to build a credit assessment mechanism that can effectively evaluate customer credit risks and help wireless service carriers to enhance the quality of debt collection processes by customizing collection strategies for various customer groups. The application of the proposed mechanism to related problems in a wireless telecommunication company in Taiwan has shown satisfactory effectiveness, accounting for a savings of $2 million of a total $500 million annual revenue. A mere 0.4 % savings is significant, given that wireless service carriers in Taiwan typically allocate 2–4 % of their revenue to uncollectible debts.

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References

  • Ahmed SR (2004) Applications of data mining in retail business. In: International conference on information technology: coding and computing, vol 2, no 2, pp 455–459

  • Bolton RN, Kannan P, Bramlett MD (2000) Implications of loyalty program membership and service experiences for customer retention and value. J Acad Mark Sci 28(1):95–108

    Article  Google Scholar 

  • Bose R (2002) Customer relationship management: key components for IT success. Indus Manage Data Syst 102(2):89–97

    Article  Google Scholar 

  • Bose I, Mahapatra RK (2001) Business data mining—a machine learning perspective. Inform Manage 39(3):211–225

    Article  Google Scholar 

  • Burez J, Van den Poel D (2007) CRM at a pay-TV company: using analytical models to reduce customer attrition by targeted marketing for subscription services. Expert Syst Appl 32(2):277–288

    Article  Google Scholar 

  • Caruana R, Niculescu-Mizil A (2004) Data mining in metric space: an empirical analysis of supervised learning performance criteria. In: Proceedings of the tenth international conference on knowledge discovery and data mining (KDD’ 04), pp 69–78

  • Chen SY, Liu X (2004) The contribution of data mining to information science. J Inform Sci 30(6):550–558

    Article  Google Scholar 

  • Chuang CL, Lin RH (2009) Constructing a reassigning credit scoring model. Expert Syst Appl 36(2):1685–1694

    Article  Google Scholar 

  • Crook JN, Edelman DB, Thomas LC (2007) Recent developments in consumer credit risk assessment. Eur J Oper Res 183(3):1447–1465

    Article  Google Scholar 

  • Director NSA, Ojha S (2004) Measuring service performance in mobile communications. Serv Ind J 24(6):109–128

    Article  Google Scholar 

  • Efrim Boritz J, Kennedy DB (1995) Effectiveness of neural network types for prediction of business failure. Expert Syst Appl 9(4):503–512

    Article  Google Scholar 

  • Fayyad U, Piatetsky-Shapiro G, Smyth P (1996) The KDD process for extracting useful knowledge from volumes of data. Commun ACM 39(11):27–34

    Article  Google Scholar 

  • Feelders A, Daniels H, Holsheimer M (2000) Methodological and practical aspects of data mining. Inform Manage 37(5):271–281

    Article  Google Scholar 

  • Fletcher D, Goss E (1993) Forecasting with neural networks: an application using bankruptcy data. Inform Manage 24(3):159–167

    Article  Google Scholar 

  • Frydman H, Schuermann T (2008) Credit rating dynamics and Markov mixture models. J Bank Finance 32(6):1062–1075

    Article  Google Scholar 

  • Hsieh NC (2004) An integrated data mining and behavioral scoring model for analyzing bank customers. Expert Syst Appl 27(4):623–633

    Article  Google Scholar 

  • Hui SC, Jha G (2000) Data mining for customer service support. Inform Manage 38(1):1–13

    Article  Google Scholar 

  • Hung SY, Yen DC, Wang HY (2006) Applying data mining to telecom churn management. Expert Syst Appl 31(3):515–524

    Article  Google Scholar 

  • Kim MK, Park MC, Jeong DH (2004) The effects of customer satisfaction and switching barrier on customer loyalty in Korean mobile telecommunication services. Telecommun Policy 28(2):145–160

    Article  Google Scholar 

  • King SF, Burgess TF (2008) Understanding success and failure in customer relationship management. Ind Mark Manage 37(4):421–431

    Article  Google Scholar 

  • Kolter P (1997) Marketing management: analysis, planning, implementation, and control. A Simon & Schuster Company, New Jersey

    Google Scholar 

  • Lacher R, Coats PK, Sharma SC, Fant LF (1995) A neural network for classifying the financial health of a firm. Eur J Oper Res 85(1):53–65

    Article  Google Scholar 

  • Larose DT (2005) Discovering knowledge in data. In: An introduction to data mining. Wiley Publishers, Hoboken

  • Lee TS, Chen IF (2005) A two-stage hybrid credit scoring model using artificial neural networks and multivariate adaptive regression splines. Expert Syst Appl 28(4):743–752

    Article  Google Scholar 

  • Lee TS, Chiu CC, Lu CJ, Chen IF (2002) Credit scoring using the hybrid neural discriminant technique. Expert Syst Appl 23(3):245–254

    Article  Google Scholar 

  • Lee JY, Kim H, Kim PJ (2010) Domain analysis with text mining: analysis of digital library research trends using profiling methods. J Inform Sci 36(2):144–161

    Article  Google Scholar 

  • Lin FR, Po RW, Orellan CVC (2011) Mining purchasing decision rules from service encounter data of retail chain stores. IseB 9(2):193–221

    Article  Google Scholar 

  • Malhotra R, Malhotra D (2003) Evaluating consumer loans using neural networks. Omega 31(2):83–96

    Article  Google Scholar 

  • Mithas S, Krishnan MS, Fornell C (2005) Why do customer relationship management applications affect customer satisfaction? J Market 69(4):201–209

    Google Scholar 

  • Noh HJ, Roh TH, Han I (2005) Prognostic personal credit risk model considering censored information. Expert Syst Appl 28(4):753–762

    Article  Google Scholar 

  • Rust RT, Moorman C, Dickson PR (2002) Getting return on quality: revenue expansion, cost reduction, or both? J Market 66(4):7–24

    Google Scholar 

  • Sörensen K, Janssens GK (2003) Data mining with genetic algorithms on binary trees. Eur J Oper Res 151(2):253–264

    Article  Google Scholar 

  • Thomas LC (2000) A survey of credit and behavioural scoring: forecasting financial risk of lending to consumers. Int J Forecast 16(2):149–172

    Article  Google Scholar 

  • Thomas L, Oliver R, Hand D (2005) A survey of the issues in consumer credit modelling research. J Oper Res Soc 56(9):1006–1015

    Article  Google Scholar 

  • Wang HF, Hong WK (2006) Managing customer profitability in a competitive market by continuous data mining. Ind Mark Manage 35(6):715–723

    Article  Google Scholar 

  • Woo KS, Fock HKY (1999) Customer satisfaction in the Hong Kong mobile phone industry. Serv Indus J 19(3):162–174

    Article  Google Scholar 

  • Yang Y (2007) Adaptive credit scoring with kernel learning methods. Eur J Oper Res 183(3):1521–1536

    Article  Google Scholar 

  • Zhang D, Zhou L (2004) Discovering golden nuggets: data mining in financial application. Syst Man Cybernet Part C Appl Rev IEEE Trans 34(4):513–522

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Information Management, National Chung Cheng University, Ming-Hsiung, Chia-Yi, 62102, Taiwan

    Hsiu-Yu Wang & Chechen Liao

  2. Department of Mathematics, National Chung Cheng University, Ming-Hsiung, Chia-Yi, 62102, Taiwan

    Cheng-Hsiung Kao

Authors
  1. Hsiu-Yu Wang
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  2. Chechen Liao
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  3. Cheng-Hsiung Kao
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Correspondence to Hsiu-Yu Wang.

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Wang, HY., Liao, C. & Kao, CH. A credit assessment mechanism for wireless telecommunication debt collection: an empirical study. Inf Syst E-Bus Manage 11, 357–375 (2013). https://doi.org/10.1007/s10257-012-0192-x

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  • DOI: https://doi.org/10.1007/s10257-012-0192-x

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