Patent ID: 11861666
Assignee: TOAST, INC.
Field: IT methods for management (Electrical engineering)
Classification: CPC G | IPC G

Claim 0:
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;
extracting the transaction features from the completed transactions set;
transforming all BINs in the set into corresponding BIN features that comprise probabilities of a given BIN falling into each of the corresponding BIN features, where each of the probabilities are determined by analyzing the historical completed transactions set;
creating a first training set comprising all transaction features, all 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 of the 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 interchange codes;
generating a lookup table that maps each combination of a set of all rounded transaction features to a corresponding predicted interchange charge, wherein:
the corresponding predicted interchange charge comprises a weighted sum of interchange charges for a subset of all of the interchange codes;
a weight for each term of the weighted sum indicates a probability that an associated one of the set of all rounded transaction features will result in a corresponding interchange code with the subset;
probabilities for weights in the weighted sum are previously determined by executing the trained random forest model; and
outputs of the table responsive to specific rounded transaction features comprises a predicted interchange charge for a corresponding transaction; and

for new transactions, extracting and rounding corresponding transation-specific features and indexing the lookup table to obtain corresponding interchange charges.