Customer credit scoring based on HMM/GMDH hybrid model |
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Authors: | Ge-Er Teng Chang-Zheng He Jin Xiao Xiao-Yi Jiang |
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Affiliation: | 1. Business School of Sichuan University, Chengdu, 610064, China 2. Department of Mathematics and Computer Science, University of Münster, Einsteinstra?e 62, 48149, Münster, Germany
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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. |
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