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A two-route CNN model for bank account classification with heterogeneous data
2019
PLoS ONE
Classifying bank accounts by using transaction data is encouraging in cracking down on illegal financial activities. However, few research simultaneously use heterogenous features, which are embedded in the time series data. In this paper, a two route convolution neural network TRHD-CNN model, fed with two types of heterogeneous feature matrices, is proposed for classifying the bank accounts. TRHD-CNN adopts divide and conquer strategy to extract characteristics from two types of data source
doi:10.1371/journal.pone.0220631
pmid:31425545
pmcid:PMC6699796
fatcat:qgodulowirf3jib3pgxk4rcrzi