Interchange code prediction system for processing credit card transactions
A computer-implemented method for predicting interchange charges includes: retrieving a historical transactions set, where each completed transaction in the set includes 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; creating a first training set including all transaction features, all pluralities of BIN features, and all true interchange codes associated with the historical transactions set; training a random forest model using the first training set and generating a second training set including 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 interchange codes; and executing the trained random forest model for new transactions to generate corresponding predicted interchange codes.
1. A computer-implemented method for predicting 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.
2. The computer-implemented method as recited in claim 1 , wherein the transaction features comprise a total transaction amount along and a tip percentage.
3. The computer-implemented method as recited in claim 2 , wherein the transaction features further comprise a time in hours between transaction authorization and transaction capture.
4. The computer-implemented method as recited in claim 3 , wherein the transaction features further comprise a type of transaction and an address verification system response.
5. The computer-implemented method as recited in claim 4 , wherein the transaction features further comprise a card network, a card type, and a merchant category code.
6. The computer-implemented method as recited in claim 1 , wherein the plurality of BIN features comprises regulated debit, debit, rewards, and high rewards.
7. The computer-implemented method as recited in claim 1 , wherein the corresponding predicted interchange codes comprise a weighted sum of interchange rates and fees for a plurality of predicted interchange codes, where each of the plurality of predicted interchange codes comprises a weight, the weight indicating the probability that a corresponding transaction will be assigned to a corresponding one of the plurality of predicted interchange codes.
8. 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.
9. The non-transitory computer-readable storage medium as recited in claim 8 , wherein the transaction features comprise a total transaction amount along and a tip percentage.
10. The non-transitory computer-readable storage medium as recited in claim 9 , wherein the transaction features further comprise a time in hours between transaction authorization and transaction capture.
11. The non-transitory computer-readable storage medium as recited in claim 10 , wherein the transaction features further comprise a type of transaction and an address verification system response.
12. The non-transitory computer-readable storage medium as recited in claim 11 , wherein the transaction features further comprise a card network, a card type, and a merchant category code.
13. The non-transitory computer-readable storage medium as recited in claim 8 , wherein the plurality of BIN features comprises regulated debit, debit, rewards, and high rewards.
14. The non-transitory computer-readable storage medium as recited in claim 8 , wherein the corresponding predicted interchange codes comprise a weighted sum of interchange rates and fees for a plurality of predicted interchange codes, where each of the plurality of predicted interchange codes comprises a weight, the weight indicating the probability that a corresponding transaction will be assigned to a corresponding one of the plurality of predicted interchange codes.
15. 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.
16. The computer program product as recited in claim 15 , wherein the transaction features comprise a total transaction amount along and a tip percentage.
17. The computer program product as recited in claim 16 , wherein the transaction features further comprise a time in hours between transaction authorization and transaction capture.
18. The computer program product as recited in claim 17 , wherein the transaction features further comprise a type of transaction and an address verification system response.
19. The computer program product as recited in claim 15 , wherein the plurality of BIN features comprises regulated debit, debit, rewards, and high rewards.
20. The computer program product as recited in claim 15 , wherein the corresponding predicted interchange codes comprise a weighted sum of interchange rates and fees for a plurality of predicted interchange codes, where each of the plurality of predicted interchange codes comprises a weight, the weight indicating the probability that a corresponding transaction will be assigned to a corresponding one of the plurality of predicted interchange codes.