Fast anti-money laundering detection method based on transaction graph

A fast anti-money laundering detection method based on a transaction graph, comprising the following steps:

• step S1, obtaining, by a processor, transaction data flows of a plurality of accounts and constructing a directed graph structure according to the transaction data flows to form a transaction graph;
• step S2, performing, by the processor, preliminary determination on the transaction graph by taking a blacklist and a money laundering rule as a benchmark, wherein if the transaction graph conforms to the blacklist and the money laundering rule, then the transaction is blocked in real time, and if the transaction graph does not conform to the blacklist and the money laundering rule, then the transaction graph is sent to a graph neural network based on location information;
• step S3, performing, by the processor, feature learning, by the graph neural network based on location information, according to a transaction feature of each node in the transaction graph, updating an unmarked transaction feature, and aggregating a node feature and a full graph feature;
• step S4, performing prediction, by the processor, executing a graph attention model in the graph neural network, on a transaction between nodes according to the node feature and the full graph feature, wherein if a prediction result is of an order of money laundering transaction is higher than or equal to a threshold value, transaction information of the transaction is sent to an anti-money laundering expert strategy center and the transaction information is presented in time through a hardware display interface, and a feedback result is sent to a historical transaction database in time;
• and if the prediction result is of the order of money laundering transaction is lower than the threshold value, then a corresponding transaction result is recorded and sent to the historical transaction database;
• and
• step S5, updating, by the processor, the graph neural network according to the high-risk and low-risk transaction results based on the historical transaction database, searching a potential transmission chain of a money laundering transaction thereby detecting a plurality of illegal nodes involved in a same money laundering case,
• wherein the formula used for updating an edge feature in the graph neural network at step S5 is:
• e′ij=NN(eij, vi, mj, vg)
• wherein e′ij denotes an edge feature after update, NN denotes a neural network comprising two fully connected layers with an activation function of ReLu, eij denotes an edge feature before update, vg denotes a feature vector corresponding to a directed graph G of the transaction graph, vi and mj denotes nodes in the directed graph, of which vi∈V, mj∈M and V={vu1, vu2, vu3, . . . , vun} denotes a series of transaction users and M={vm1, vm2, vm3, . . . , vmn} denotes a series of merchants,
• wherein the formula used for updating a node feature in the graph neural network at step S5 is:
• v′ui=NN(Σj∈Nieij, vui, vg)
• v′mi=NN(Σj∈Nieij, vmi, vg)
• wherein v′ui and v′mi denote a user node feature and a merchant node feature after update, respectively;
• vui and vmi denote a user node feature and a merchant node feature before update, respectively;
• and Ni denotes all edges connected to the node vi,
• wherein the formula used for updating a feature vector of the directed graph G in the graph neural network at step S5 is:
• v′g=NN(vu, vm, ē, vg)
• wherein v′g denotes a feature vector of the directed graph G after update, vu denotes the mean of all user node features in the directed graph, vm denotes the mean of all merchant node features in the directed graph, and ē denotes the mean of all edges in the directed graph,
• wherein the graph neural network based on location information comprises an attention mechanism based graph convolutional network, accepting X∈N1*N2 as an input, wherein X denotes an edge feature constructed in the graph neural network, N1 denotes the dimension of a temporal window, N2 denotes the feature dimension of vf, vf denotes the feature vector obtained by concatenating v′g, v′ui, v′mi, and e′ij,
• wherein the graph convolutional network is provided with a temporal attention layer, specifically shown in the following formula:
• rept
• =
• ∑
• t
• =
• 1
• N
• 1
• β
• t
• ⁢
• χ
• ⁡
• (
• t
• ,
• :
• )
• β
• t
• =
• exp
• ⁡
• (
• 1
• -
• λ
• 1
• )
• *
• (
• N
• ⁢
• N
• ⁡
• (
• W
• n
• ,
• χ
• ⁡
• (
• t
• ,
• :
• )
• )
• )
• ∑
• i
• =
• 1
• N
• 1
• ⁢
• exp
• ⁡
• (
• 1
• -
• λ
• 1
• )
• *
• (
• N
• ⁢
• N
• ⁡
• (
• W
• n
• ,
• χ
• ⁡
• (
• i
• ,
• :
• )
• )
• )
• wherein βt,j denotes the weight parameter of the temporal window t, NN denotes a feed forward network, Wn denotes a parameter that needs to be trained for the temporal attention network, λ1 denotes a process parameter, and rept denotes an output result of each transaction in the graph convolutional network.