Research And Application Of The Improved Decision Tree Algorithm On The Bank Abnormal Transaction Recognition

In recent years,with the development of the financial industry,the illegal transactions in commercial banks' business are also more and more serious.Identifying abnormal transaction accounts in a large amount of transaction data has become an important work of commercial banks in maintaining normal financial order.The method for commercial banks to identify abnormal transaction data is that relevant practitioners manually formulate the identification criteria and manually set the identification criteria parameters based on the historical business experience nowadays.The accuracy and effectiveness of such identification method is easily affected by the quality of the relevant practitioners and the small-probability events on the historic business transaction data,so this paper uses the statistical learning method to reduce the artificial factors in the identification work.Decision tree model has been widely used as a statistical learning method in recognition work because of its readability and fast classification speed.The main work of this paper is applying the decision tree model in identifying the abnormal transaction account of commercial banks.This paper includes the followings:Firstly,the transaction network that from the commercial bank transaction data is built.The nodes and edges in the transaction network correspond to the transaction accounts and the transaction activities between transaction accounts in the total transaction data.Taking every node in the transaction network as a unit,the structure and properties of the nodes could be extracted from the transaction network and count the structure and properties of the nodes.Then the structure and properties of the nodes are used as a feature set for generating the decision tree model,and the statistics of nodes' structure and properties are used as the data set for generating decision trees.The identification of transaction records in off-site monitoring mode could be transformed into the identification of transaction accounts,then the amount of data in the job of identification could be reduced.These could build the feature set and the data set for generating the decision tree model.Then,a noise data identification model that could be suitable for the research background of this paper has been built.Taking every transaction account as a unit,for each transaction account in the transaction network,all the noise data in the transaction records can be effectively identified and cleared.These can optimize the feature set and the data set for generating the decision tree model.Finally,the structure and properties of nodes in the transaction network are used as the feature set,and the statistical data of nodes' structure and properties are used as the data set.The improving in the current C4.5 algorithm can make the calculation of thresholds in discrete process of continuity characteristics more accurately and reduce the number of discrete divisions by preserving the most data.A decision tree model for identifying abnormal transactions in commercial banks can be generated by the improved C4.5 algorithm.The decision tree model can be applied in the work of identifying abnormal transactions.