Research And Application Of Prediction Model Of Customer Arrears Based On Hybrid Markov And Bayesian

With the rapid development of the service industry in our country, people's work and life become more convenient. However, the phenomenon of arrearages in long-term and post-paid service is also increasing, such as Telecommunications, electricity, gas and so on.Which result in the loss of enterprise economic losses and assets. Moreover, it makes its operating costs increasing and affects the business development of the enterprise and cause the enterprise can't provide more quality services to the society. Moreover, it makes its operating costs increasing and affects the business development of the enterprise and cause the enterprise can't provide more quality services to the society. In order to resolve the problems e ffectively, mass data accumulated by service enterprises can be used to find the potential regulations and the main effect factors of own user behavior patterns. By using a variety of data analysis tools and building the mathematical prediction model to carry out the forecast analysis for the possibility of user arrear.In this connection, this paper proposes the customer arrears predict model that based on hybrid Markov and Bayesian. It aims at those post-paid business that the type of payment are long-term and timing, and to avoid the customer owes arrears and realize the differentiation effectively. This model takes multi factors of all customers into consideration and gives the possibility of customers who may own fee. Moreover, it provides comprehensive and objective and precise decision information for user arrear early-warning and disposal. Thus, it supports different disposal with different customers. The main work of this paper: first of all, based on the payment characteristics to establish the k order Markov model and compute the initial delinquent probability of the customers; then, the essential attribute, behavioral characteristics and arrearage information of customers are fused together to generate the Bayesian network based on conditional mutual information and mountain climbing method. Modifying the initial delinquent probability and forming the final customer arrears probability. Then, through experiment by using the real data to prove that this prediction model has a good efficient on customer prediction. Finally, we give a detailed analysis on the functional requirements of the Telecommunication difference reminder system and give the architecture design of the system. What is more, we design and implement above research achievements, which is applied to the system.