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Knowledge and Information Systems

, Volume 36, Issue 3, pp 731–747 | Cite as

Customer credit scoring based on HMM/GMDH hybrid model

  • Ge-Er Teng
  • Chang-Zheng HeEmail author
  • Jin Xiao
  • Xiao-Yi Jiang
Regular Paper

Abstract

Hidden Markov model (HMM) has made great achievements in many fields such as speech recognition and engineering. However, due to its assumption of state conditional independence between observations, HMM has a very limited capacity for recognizing complex patterns involving more than first-order dependencies in customer relationships management. Group Method of Data Handling (GMDH) could overcome the drawbacks of HMM, so we propose a hybrid model by combining the HMM and GMDH to score customer credit. There are three phases in this model: training HMM with multiple observations, adding GMDH into HMM and optimizing the hybrid model. The proposed hybrid model is compared with other exiting methods in terms of average accuracy, Type I error, Type II error and AUC. Experimental results show that the proposed method has better performance than HMM/ANN in two credit scoring datasets. The implementation of HMM/GMDH hybrid model allows lenders and regulators to develop techniques to measure customer credit risk.

Keywords

Hybrid model HMM GMDH Credit scoring CRM 

Notes

Acknowledgments

This research is supported by the Natural Science Foundation of China under Grant Nos. 71071101, 71101100 and 71211130018, New Teachers Fund for Doctor Stations, Ministry of Education under Grant No. 20110181120047, China Postdoctoral Science Foundation under Grant No. 2011M500418, Research Start-up Project of Sichuan University under Grant No. 2010SCU11012.

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Copyright information

© Springer-Verlag London 2012

Authors and Affiliations

  • Ge-Er Teng
    • 1
  • Chang-Zheng He
    • 1
    Email author
  • Jin Xiao
    • 1
  • Xiao-Yi Jiang
    • 2
  1. 1.Business School of Sichuan UniversityChengduChina
  2. 2.Department of Mathematics and Computer ScienceUniversity of MünsterMünsterGermany

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