Machine Learning based on Post-Transaction Data

Abstract

Techniques are disclosed relating to classifying transactions using post-transaction information. Training architecture may be used to train a first classifier module using first data for a set of transactions as training data input, where the first data includes both pre-transaction information and post-transaction information for transactions in the set of transactions. During training of the first classifier module, in disclosed techniques, correct classifications for the transaction in the set of transactions are known. The training architecture, in disclosed techniques, generates respective weights for multiple transactions in the set of transactions based on classification outputs of the trained first classifier for the multiple transactions. In disclosed techniques, the training architecture trains a second classifier module, based on the generated weights, using second data for the set of transactions as training data input. In some embodiments, the trained second classifier module classifies one or more pending transactions based only on pre-transaction information for the one or more pending transactions.

1 . (canceled) 
     
     
         2 . A method, comprising:
 receiving, at a trained second classifier module of a computer system, an indication of a pending transaction initiated by a user computing device,   wherein a first classifier module was trained using first data for a set of completed transactions as training data input, wherein the first data includes, for respective transactions in the set of completed transactions both pre-transaction information and post-transaction information, wherein labeled classifications for transactions in the set of completed transactions are known, and wherein the post-transaction information for a first transaction in the set of completed transactions includes information for one or more transactions performed subsequent to a completion of the first transaction and non-transaction activity occurring subsequent to the completion of the first transaction for a user associated with the first transaction,   wherein the trained second classifier module was trained using operations comprising:
 generating respective weights for multiple transactions in the set of completed transactions based on classification outputs of the trained first classifier for the multiple transactions; and 
 training, based on the generated weights, a second classifier module using second data for the set of completed transactions as training data input, wherein the second data for the set of completed transactions includes pre-transaction information for transactions in the set of completed transactions; 
   classifying, using the trained second classifier module of the computer system, the pending transaction based on pre-transaction information for the pending transaction; and   the computer system generating, based on a classification output by the trained second classifier for the pending transaction, an authorization decision for the pending transaction, wherein the authorization decision specifies an indication of approval or non-approval for the pending transaction.   
     
     
         3 . The method of  claim 2 , wherein the post-transaction information includes location data for a plurality of user devices. 
     
     
         4 . The method of  claim 2 , wherein the post-transaction information includes Internet browsing history data associated with a plurality of user devices. 
     
     
         5 . The method of  claim 2 , wherein the post-transaction information includes textual data input by a plurality of users associated with the post-transaction information. 
     
     
         6 . The method of  claim 2 , wherein the pending transaction is an electronic purchase transaction initiated by the user computing device via a software program executing on the user computer device. 
     
     
         7 . The method of  claim 2 , wherein the training based on the generated weights includes one or more of:
 performing a greater number of training iterations for a first transaction than for a second transaction based on the first transaction having a greater weight than the second transaction; or   performing a greater training adjustment for a first transaction than for a second transaction based on the first transaction having a greater weight than the second transaction.   
     
     
         8 . The method of  claim 2 , wherein the generating the respective weights is based on relationships between labeled classifications and output predictions generated by the trained first classifier module for the transactions. 
     
     
         9 . The method of  claim 2 , wherein the generating the respective weights provides greater weights for transactions whose output predictions are further from a labeled classification. 
     
     
         10 . A non-transitory computer-readable medium having stored thereon instructions that are executable by a computer system having a processor and a memory to cause the computer system to perform operations comprising:
 receiving, at a trained second classifier module of the computer system, an indication of a pending transaction initiated by a user computing device,   wherein a first classifier module was trained using first data for a set of completed transactions as training data input, wherein the first data includes, for respective transactions in the set of completed transactions both pre-transaction information and post-transaction information, wherein labeled classifications for transactions in the set of completed transactions are known, and wherein the post-transaction information for a first transaction in the set of completed transactions includes information for one or more transactions performed subsequent to a completion of the first transaction and non-transaction activity occurring subsequent to the completion of the first transaction for a user associated with the first transaction,   wherein the trained second classifier module was trained using operations comprising:
 generating respective weights for multiple transactions in the set of completed transactions based on classification outputs of the trained first classifier for the multiple transactions; and 
 training, based on the generated weights, a second classifier module using second data for the set of completed transactions as training data input, wherein the second data for the set of completed transactions includes pre-transaction information for transactions in the set of completed transactions; 
   classifying, using the trained second classifier module of the computer system, the pending transaction based on pre-transaction information for the pending transaction; and   the computer system generating, based on a classification output by the trained second classifier for the pending transaction, an authorization decision for the pending transaction, wherein the authorization decision specifies an indication of approval or non-approval for the pending transaction.   
     
     
         11 . The non-transitory computer-readable medium of  claim 10 , wherein the post-transaction information includes textual data input by a plurality of users associated with the post-transaction information. 
     
     
         12 . The non-transitory computer-readable medium of  claim 10 , wherein the pending transaction is an electronic purchase transaction initiated by the user computing device via a software program executing on the user computer device. 
     
     
         13 . The non-transitory computer-readable medium of  claim 10 , wherein the training based on the generated weights includes one or more of:
 performing a greater number of training iterations for a first transaction than for a second transaction based on the first transaction having a greater weight than the second transaction; or   performing a greater training adjustment for a first transaction than for a second transaction based on the first transaction having a greater weight than the second transaction.   
     
     
         14 . The non-transitory computer-readable medium of  claim 10 , wherein the generating the respective weights is based on relationships between labeled classifications and output predictions generated by the trained first classifier module for the transactions. 
     
     
         15 . The non-transitory computer-readable medium of  claim 10 , wherein the generating the respective weights provides greater weights for transactions whose output predictions are further from a labeled classification. 
     
     
         16 . A computer system, comprising:
 a processor;   a network interface; and   a non-transitory computer-readable medium having stored thereon instructions executable by the computer system to cause the computer system to perform operations comprising:   receiving, at a trained second classifier module of the computer system, an indication of a pending transaction initiated by a user computing device,   wherein a first classifier module was trained using first data for a set of completed transactions as training data input, wherein the first data includes, for respective transactions in the set of completed transactions both pre-transaction information and post-transaction information, wherein labeled classifications for transactions in the set of completed transactions are known, and wherein the post-transaction information for a first transaction in the set of completed transactions includes information for one or more transactions performed subsequent to a completion of the first transaction and non-transaction activity occurring subsequent to the completion of the first transaction for a user associated with the first transaction,   wherein the trained second classifier module was trained using operations comprising:
 generating respective weights for multiple transactions in the set of completed transactions based on classification outputs of the trained first classifier for the multiple transactions; and 
 training, based on the generated weights, a second classifier module using second data for the set of completed transactions as training data input, wherein the second data for the set of completed transactions includes pre-transaction information for transactions in the set of completed transactions; 
   classifying, using the trained second classifier module of the computer system, the pending transaction based on pre-transaction information for the pending transaction; and   the computer system generating, based on a classification output by the trained second classifier for the pending transaction, an authorization decision for the pending transaction, wherein the authorization decision specifies an indication of approval or non-approval for the pending transaction.   
     
     
         17 . The computer system of  claim 16 , wherein the training the second classifier module based on the generated weights is performed using gradient boosting tree machine learning techniques. 
     
     
         18 . The computer system of  claim 16 , wherein the training based on the generated weights includes one or more of:
 performing a greater number of training iterations for a first transaction than for a second transaction based on the first transaction having a greater weight than the second transaction; or   performing a greater training adjustment for a first transaction than for a second transaction based on the first transaction having a greater weight than the second transaction.   
     
     
         19 . The computer system of  claim 16 , wherein the generating the respective weights is based on relationships between labeled classifications and output predictions generated by the trained first classifier module for the transactions. 
     
     
         20 . The computer system of  claim 16 , wherein the generating the respective weights provides greater weights for transactions whose output predictions are further from a labeled classification. 
     
     
         21 . The computer system of  claim 16 , wherein the post-transaction information includes textual data input by a plurality of users associated with the post-transaction information.