A prediction model of customer arrears based on hybrid Markov and Bayesian

doi:10.3969/j.issn.1000-1565.2016.05.014

Abstract

Abstract: In order to analyze the post-paid services with the characteristics of long term and on time pay,we put forward a prediction model based on hybrid Markov and Bayesian. It is based on the multi-factor information gain of all the customers,and computes the potential owe customers' probability of arrears.Moreover,it can provide comprehensive,objective and subtle decision information to the warning of customer arrears and disposal,and it can support differentiation treatment.First of all,we build the k-order Markov model based on the characteristics of the pay,then calculate the customers' initial probability.Secondly,we merge the customers' basic attributes,behavior feature and own information.Then,using the conditional mutual information and the hill climbing algorithm to generate the target Bayesian network to modify the initial probability of arrears,which form the final client own probability.Finally,through experiment by using the real data,we prove that this predict model is efficient in customer prediction.

Key words: post-paid customer, probability prediction model, hybrid Markov, Bayesian

CLC Number:

TP181

WU Shuxia,CHEN Lian,GAO Shengbao. A prediction model of customer arrears based on hybrid Markov and Bayesian[J]. Journal of Hebei University (Natural Science Edition), 2016, 36(5): 535-540.

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