HMM/GMDH hybrid model

Customer credit scoring based on HMM/GMDH hybrid model


Ge-Er Teng · Chang-Zheng He · Jin Xiao ·Xiao-Yi Jiang


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 condi-
tional independence between observations, HMM has a very limited capacity for recogniz-
ing 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 cus-
tomer credit. There are three phases in thismodel: training HMMwithmultiple 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.