Neural embeddings of transaction data

Abstract

Systems, methods, and computer program products to provide neural embeddings of transaction data. A network graph of transaction data based on a plurality of transactions may be received. The network graph of transaction data may define relationships between the transactions, each transaction associated with at least a merchant and an account. A neural network may be trained based on training data comprising a plurality of positive entity pairs and a plurality of negative entity pairs. An embedding function may then encode transaction data for a first new transaction. An embeddings layer of the neural network may determine a vector for the first new transaction based on the encoded transaction data for the first new transaction. A similarity between the vectors for the transactions may be determined. The first new transaction may be determined to be related to the second transaction based on the similarity.

What is claimed is: 
     
         1 . A method, comprising:
 encoding, by an application executing on a processor based on an embedding function, transaction data for a first transaction;   determining, by an embeddings layer of a neural network executing on the processor and based on the encoded transaction data for the first transaction, a vector for the first transaction, wherein the neural network is based on a plurality of positive entity pairs from a network graph of transaction data and a plurality of negative entity pairs not present in the network graph of transaction data, the negative entity pairs comprising artificially generated relationships between each entity in the negative entity pair;   processing, by the application, the first transaction based on the vector and an embedding space for the vector to determine one or more of: (i) the first transaction is a fraudulent transaction, (ii) that the first transaction is associated with money laundering, or (iii) a recommendation based on the first transaction; and   transmitting, by the application, a result of the processing of the first transaction.   
     
     
         2 . The method of  claim 1 , wherein the first transaction is associated with a first account, wherein processing the first transaction comprises:
 determining, by the application, a distance in the embedding space between the vector for the first transaction and a vector for a second transaction, the second transaction associated with the first account;   determining, by the application, that the distance exceeds a threshold distance;   determining, by the application based on the distance exceeding the threshold distance, that the first transaction is the fraudulent transaction; and   applying, by the application, an indication of the fraudulent transaction to the first account.   
     
     
         3 . The method of  claim 1 , wherein the first transaction is associated with a first account, wherein processing the first transaction comprises:
 clustering, by the application based on a model trained based on the neural network, the first transaction into a first cluster of transactions;   determining, by the application, that other transactions in the first cluster of transactions are associated with one or more accounts that have engaged in money laundering; and   determining, by the application based on the clustering of the first transaction into the first cluster of transactions and the determination that the other transactions in the first cluster of transactions are associated with one or more accounts that have engaged in money laundering, that the first transaction is associated with the money laundering.   
     
     
         4 . The method of  claim 1 , wherein the first transaction is associated with a first account, a first merchant, and a first location, wherein processing the first transaction comprises:
 determining, by the embeddings layer of the neural network based on encoded transaction data for a second transaction, a second merchant associated with a second location; and   generating, by the application for the first account, the recommendation specifying the second merchant associated with the second location.   
     
     
         5 . The method of  claim 1 , wherein the first transaction is associated with a first account, wherein processing the first transaction comprises:
 determining, by the application, a similarity between the vector for the first transaction and a vector for a second transaction associated with a second account;   determining, by the application based on the similarity between the vector for the first transaction and the vector for the second transaction, that the first account is similar to the second account; and   generating, by the application based on the determination that the first account is similar to the second account, the recommendation comprising the second account.   
     
     
         6 . The method of  claim 1 , wherein the application processes the first transaction based at least in part on a model, wherein the model is trained based on the embeddings layer of the neural network. 
     
     
         7 . The method of  claim 1 , the neural network comprising an embeddings layer, wherein the plurality of positive entity pairs are based on a respective time between a respective timestamp of the transactions of each positive entity pair being less than a time threshold. 
     
     
         8 . The method of  claim 7 , wherein the network graph of transaction data is based on a plurality of transactions, the network graph of transaction data defining relationships between the plurality of transactions, each transaction associated with at least a merchant and one account of a plurality of accounts, the plurality of transactions excluding the first transaction. 
     
     
         9 . A non-transitory computer-readable storage medium, the computer-readable storage medium including instructions that when executed by a processor, cause the processor to:
 encode, by an application based on an embedding function, transaction data for a first transaction;   determine, by an embeddings layer of a neural network and based on the encoded transaction data for the first transaction, a vector for the first transaction, wherein the neural network is based on a plurality of positive entity pairs from a network graph of transaction data and a plurality of negative entity pairs not present in the network graph of transaction data;   process, by the application, the first transaction based on the vector and an embedding space for the vector; and   transmit, by the application, a result of the processing of the first transaction.   
     
     
         10 . The computer-readable storage medium of  claim 9 , wherein the first transaction is associated with a first account, wherein the instructions to process the first transaction comprises instructions that when executed by the processor, cause the processor to:
 determine, by the application, a distance in the embedding space between the vector for the first transaction and a vector for a second transaction, the second transaction associated with the first account;   determine, by the application, that the distance exceeds a threshold distance;   determine, by the application based on the distance exceeding the threshold distance, that the first transaction is a fraudulent transaction; and   apply, by the application, an indication of the fraudulent transaction to the first account.   
     
     
         11 . The computer-readable storage medium of  claim 9 , wherein the first transaction is associated with a first account, wherein the instructions to process the first transaction comprises instructions that when executed by the processor, cause the processor to:
 cluster, by the application based on a model trained based on the neural network, the first transaction into a first cluster of transactions;   determine, by the application, that other transactions in the first cluster of transactions are associated with one or more accounts that have engaged in money laundering; and   determine, by the application based on the clustering of the first transaction into the first cluster of transactions and the determination that the other transactions in the first cluster of transactions are associated with one or more accounts that have engaged in money laundering, that the first transaction is associated with money laundering.   
     
     
         12 . The computer-readable storage medium of  claim 9 , wherein the first transaction is associated with a first account, a first merchant, and a first location, wherein the instructions to process the first transaction comprises instructions that when executed by the processor, cause the processor to:
 determine, by the embeddings layer of the neural network based on encoded transaction data for a second transaction, a second merchant associated with a second location; and   generate, by the application for the first account, a recommendation specifying the second merchant associated with the second location.   
     
     
         13 . The computer-readable storage medium of  claim 9 , wherein the first transaction is associated with a first account, wherein the instructions to process the first transaction comprises instructions that when executed by the processor, cause the processor to:
 determine, by the application, a similarity between the vector for the first transaction and a vector for a second transaction associated with a second account;   determine, by the application based on the similarity between the vector for the first transaction and the vector for the second transaction, that the first account is similar to the second account; and   generate, by the application based on the determination that the first account is similar to the second account, a recommendation comprising the second account.   
     
     
         14 . The computer-readable storage medium of  claim 9 , wherein the application processes the first transaction based at least in part on a model, wherein the model is trained based on the embeddings layer of the neural network. 
     
     
         15 . The computer-readable storage medium of  claim 9 , the negative entity pairs comprising artificially generated relationships between each entity in the negative entity pair, the neural network comprising an embeddings layer, wherein the plurality of positive entity pairs are based on a respective time between a respective timestamp of the transactions of each positive entity pair being less than a time threshold. 
     
     
         16 . A computing apparatus comprising:
 a processor; and   a memory storing instructions that, when executed by the processor, cause the processor to:
 encode, by an application based on an embedding function, transaction data for a first transaction; 
 determine, by an embeddings layer of a neural network and based on the encoded transaction data for the first transaction, a vector for the first transaction, wherein the neural network is based on a plurality of positive entity pairs from a network graph of transaction data and a plurality of negative entity pairs not present in the network graph of transaction data; 
 process, by the application, the first transaction based on the vector and an embedding space for the vector; and 
 transmit, by the application, a result of the processing of the first transaction. 
   
     
     
         17 . The computing apparatus of  claim 16 , wherein the first transaction is associated with a first account, wherein the instructions to process the first transaction comprises instructions that when executed by the processor, cause the processor to:
 determine, by the application, a distance in the embedding space between the vector for the first transaction and a vector for a second transaction, the second transaction associated with the first account;   determine, by the application, that the distance exceeds a threshold distance;   determine, by the application based on the distance exceeding the threshold distance, that the first transaction is a fraudulent transaction; and   apply, by the application, an indication of the fraudulent transaction to the first account.   
     
     
         18 . The computing apparatus of  claim 16 , wherein the first transaction is associated with a first account, wherein the instructions to process the first transaction comprises instructions that when executed by the processor, cause the processor to:
 cluster, by the application based on a model trained based on the neural network, the first transaction into a first cluster of transactions;   determine, by the application, that other transactions in the first cluster of transactions are associated with one or more accounts that have engaged in money laundering; and   determine, by the application based on the clustering of the first transaction into the first cluster of transactions and the determination that the other transactions in the first cluster of transactions are associated with one or more accounts that have engaged in money laundering, that the first transaction is associated with money laundering.   
     
     
         19 . The computing apparatus of  claim 16 , wherein the first transaction is associated with a first account, a first merchant, and a first location, wherein the instructions to process the first transaction comprises instructions that when executed by the processor, cause the processor to:
 determine, by the embeddings layer of the neural network based on encoded transaction data for a second transaction, a second merchant associated with a second location; and   generate, by the application for the first account, a recommendation specifying the second merchant associated with the second location.   
     
     
         20 . The computing apparatus of  claim 16 , wherein the first transaction is associated with a first account, wherein the instructions to process the first transaction comprises instructions that when executed by the processor, cause the processor to:
 determine, by the application, a similarity between the vector for the first transaction and a vector for a second transaction associated with a second account;   determine, by the application based on the similarity between the vector for the first transaction and the vector for the second transaction, that the first account is similar to the second account; and   generate, by the application based on the determination that the first account is similar to the second account, a recommendation comprising the second account.