Research On Prediction Method In Exceptional Exchange Of The Dynamic Financial Network

Financial crimes always have the attributes of covertness, intelligence and professionalism, which are more serious in money-laundering. At present, most systems take use of traditional knowledge discovery. Firstly, we propose interested suppose by investigating data set; and then, analysis approach will be designed to solve these problems. The approach can always find the abnormal exchange paths and the abnormal exchange nodes, but there is still a big disadvantage that we always make use of the static method and in the dynamic financial networks we can't predict the situation of the exchange in the future, so it is hard to prevent the illegal trade. For this problem, we make use of Markov chain theory and Link-prediction technology to design a prediction method.According to anti-money-laundering knowledge and experts' summarization, the question for discussion focuses on five node object type as follows: accounts, money, organization, trade time, trade address. We make use of these five node type and relationship to design a prediction method. The flow includes two phases as follow: phase of predicting abnormal exchange node, phase of predicting abnormal exchange path.The phase of predicting abnormal exchange node bases on Markov chain theory. Markov chain is an extensively applied stochastic process model which is to quantitatively analysis a system transferring from one state to another, this paper first introduces the basic theory, after that it talks about the transition probability, finally, it uses the Markov chain prediction method based on absolute distribution to design a prediction method and makes experiments to prove the method is practical and can be applied in this field.The phase of predicting abnormal exchange path bases on Link-prediction theory. This paper summarizes so many attributes based on the topology of the graph which can be used in the link-prediction problem, we use a new attribute based on the exchange times of the common neighbors which is suitable for the exchange networks and build the predictor from the new attribute. Finally we make experiments to prove the new attribute is better than the traditional common neighbors attribute and the method is practical and can be applied in this field.