Customer credit scoring based on HMM/GMDH hybrid model

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.