| CPC G06Q 30/0204 (2013.01) [G06Q 10/10 (2013.01); G06Q 30/0224 (2013.01); G06Q 40/02 (2013.01); G06Q 40/12 (2013.12)] | 15 Claims |
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1. A method for determining and validating a dominant account profile classification model by matching first and second training data, the method comprising:
conducting, at a plurality of points of sale, transaction data without initially using any dominant account profile, by associating account identifiers with tokens stored in one or more data structures;
receiving, with at least one processor, the transaction data;
associating, with the at least one processor, the transaction data with a plurality of payment transactions conducted by a user within a predetermined time interval after activation of a debit account involved in the plurality of payment transactions;
generating, with the at least one processor, a dominant account profile classification model, wherein the dominant account profile classification model is configured to provide an output that includes a prediction as to whether the user will use a particular account associated with the user to conduct a threshold value of payment transactions in one or more payment transaction categories of a plurality of payment transaction categories during the predetermined time interval, wherein generating the dominant account profile classification model comprises:
processing the transaction data to obtain training data for the dominant account profile classification model, wherein processing the transaction data comprises:
determining a set of transaction variables based on the transaction data, wherein the set of transaction variables comprises at least one of:
a first account activation variable associated with whether a first account of the user was involved in a first payment transaction conducted in a first payment transaction category,
a second account activation variable associated with a specific number of the plurality of payment transaction categories in which a second payment transaction involving a second account of the user was conducted,
a third account activation variable associated with a first number of payment transactions involving a third account of the user in a second payment transaction category,
a fourth account activation variable associated with a first transaction volume of the plurality of payment transactions involving a fourth account of the user in a third payment transaction category,
a fifth account activation variable associated with a determination of whether a fifth account of the user was involved in a first type of payment transaction,
a sixth account activation variable associated with a second number of payment transactions involving a sixth account of the user based on a second type of payment transaction,
a seventh account activation variable associated with a second transaction volume of the plurality of payment transactions involving a seventh account of the user based on a third type of payment transaction,
an eighth account activation variable associated with a determination of whether an eighth account of the user was involved in a particular payment transaction during a first time interval, the eighth account activation variable associated with a third number of payment transactions involving the eighth account of the user during the first time interval,
a ninth account activation variable associated with a third transaction volume associated with the plurality of payment transactions involving a ninth account of the user during a second time interval,
a tenth account activation variable associated with a determination of whether a tenth account of a plurality of accounts of the user was involved in a specific payment transaction,
an eleventh account activation variable associated with a fourth number of payment transactions involving an eleventh account of the plurality of accounts of the user as compared to a fifth number of payment transactions involving the plurality of accounts of the user,
a twelfth account activation variable associated with a fourth transaction volume of the plurality of payment transactions involving the plurality of accounts the of user, or
any combination thereof;
storing the dominant account profile classification model in the one or more data structures;
identifying a subset of the set of the transaction variables for determining a probability that the user will conduct the threshold value of the payment transactions; and
changing the transaction data into a format to be analyzed to generate the dominant account profile classification model, wherein the training data is partitioned into a first portion and a second portion;
generating a first output from the dominant account profile classification model based on the subset of the set of transaction variables and the first portion of the training data;
generating a second output from the dominant account profile classification model based on the subset of the set of transaction variables and the second portion of the training data; and
validating the dominant account profile classification model by matching the first output and the second output from the dominant account profile classification model;
determining, with the at least one processor, a plurality of prediction scores for the particular account based on the dominant account profile classification model and the transaction data, wherein determining the plurality of prediction scores comprises:
determining, with the at least one processor, for the user, a prediction score for each dominant account profile of a plurality of dominant account profiles, wherein the prediction score for a dominant account profile comprises a prediction of whether the user will conduct the threshold value of payment transactions using the particular account in the one or more payment transaction categories of the plurality of payment transaction categories; and
determining, with the at least one processor, a highest prediction score of the plurality of prediction scores for the plurality of dominant account profiles, wherein the highest prediction score corresponds to a recommended dominant account profile of the plurality of dominant account profiles for the particular account;
generating, with the at least one processor, at least one report associated with the recommended dominant account profile of the plurality of dominant account profiles for the particular account based on determining the recommended dominant account profile;
communicating, with the at least one processor, the at least one report based on generating the at least one report;
determining, with the at least one processor, a location of the user based on: location data received from a GPS equipped user device, an online search of the user at the user device, and the transaction data associated with the plurality of payment transactions involving the user;
determining, with the at least one processor, merchant identity data associated with a merchant located within a predetermined distance from the determined location of the user;
generating, with the at least one processor, an offer of the merchant based on determining that the prediction score of the recommended dominant account profile satisfies a threshold value of the prediction score;
transmitting, with the at least one processor, the offer to the user based on the determined merchant identity and the recommended dominant account profile;
generating, with the at least one processor, a new training data based on whether the user conducted the payment transaction using the particular account associated with the user based on the offer within the predetermined time interval; and
updating, with the at least one processor, the dominant account profile classification model based on the new training data.
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