Interchange code prediction system for processing credit card transactions

A non-transitory computer-readable storage medium storing instructions that, when executed by a computer, cause the computer to perform a method for using a computer system to predict interchange codes corresponding to credit card transactions, the method comprising:

• retrieving a historical completed transactions set from a database that corresponds to a plurality of merchants, where each completed transaction in the set comprises transaction features, a bank identification number (BIN), and a corresponding true interchange code;
• transforming all BINs in the set into a corresponding plurality of BIN features that comprise probabilities of a given BIN falling into each of the corresponding plurality of BIN features;
• creating a first training set comprising all transaction features, all pluralities of BIN features, and all true interchange codes associated with the historical completed transactions set;
• training a random forest model using the first training set, wherein said training comprises rounding of all BIN features and non-discrete ones of the transaction features, and generating a second training set comprising rounded BIN features, rounded transaction features, discrete ones of the transaction features, and the true interchange codes;
• training the random forest model using the second training set to generate a trained random forest model for prediction of the true interchange codes;
• and
• executing the trained random forest model for new transactions to generate corresponding predicted interchange codes.

A computer program product for predicting interchange codes corresponding to credit card transactions, the computer program product comprising:

• a computer readable non-transitory medium having computer readable program code stored thereon, the computer readable program code comprising:
• program instructions that, when executed by a processor, cause the processor to retrieve a historical completed transactions set from a database that corresponds to a plurality of merchants, where each completed transaction in the set comprises transaction features, a bank identification number (BIN), and a corresponding true interchange code;
• program instructions that, when executed by a processor, cause the processor to transform all BINs in the set into a corresponding plurality of BIN features that comprise probabilities of a given BIN falling into each of the corresponding plurality of BIN features;
• program instructions that, when executed by a processor, cause the processor to create a first training set comprising all transaction features, all pluralities of BIN features, and all true interchange codes associated with the historical completed transactions set;
• program instructions that, when executed by a processor, cause the processor to train a random forest model using the first training set, wherein said training comprises rounding of all BIN features and non-discrete ones of the transaction features, and generating a second training set comprising rounded BIN features, rounded transaction features, discrete ones of the transaction features, and the true interchange codes;
• program instructions that, when executed by a processor, cause the processor to train the random forest model using the second training set to generate a trained random forest model for prediction of the true interchange codes;
• and
• program instructions that, when executed by a processor, cause the processor to execute the trained random forest model for new transactions to generate corresponding predicted interchange codes.