Technique to aggregate merchant level information for use in a supervised learning model to detect recurring trends in consumer transactions

A system, comprising:

• a memory for storing instructions;
• and
• a processor, communicatively coupled to the memory, configured to execute the instructions, the instructions causing the processor to:
• store information gathered from a plurality of data sources in a database, the information comprising merchant identification information of a plurality of merchants and a plurality of transactions of a plurality of customer accounts is gathered for categorizing a transaction recurrency between a merchant of the plurality of merchants and customer account of the plurality of customer accounts;
• aggregate a plurality of sets of transactions based on the gathered information stored in the database, each set of transactions is related to an account-merchant pairing, wherein the account-merchant pairing includes a customer account of the plurality of customer accounts and a merchant of the plurality of merchants;
• split each set of the transactions into a plurality of first subset of transactions and corresponding second subset of transactions for generating expanded test data sets, wherein transactions are split based on a split date;
• train a machine learning algorithm by using target label variables, the target label variables are generated based on the second subset of transactions according to predicted transaction dates in the second subset of transactions of the corresponding first subset of transactions of the expanded test data sets and predetermined matching criteria for training a machine learning algorithm, wherein the target label variables identify recurrency of transactions in the first subset of transactions, wherein the matching criteria comprises a number of predictions and a date tolerance, and wherein the predicted transaction dates in the second subset of transactions within the date tolerance are at least the number of predictions in the matching criteria;
• convert transactions dates of the transactions in each first subset of transactions into ordinal dates;
• map the ordinal dates into a phase space, wherein the phase space represents a billing cycle;
• determine a mean coordinate based on the mapped ordinal dates;
• determine a vector strength, a coverage, and a redundancy, wherein the vector strength, the coverage, and the redundancy are phase variables characterizing a recurrence period;
• predict the recurrence period for a transaction related to the account-merchant pairing for each customer of the plurality of customers;
• aggregate another set of transactions between the plurality of customers and the merchant, wherein the another set of transactions comprises transactions in the plurality of sets of transactions;
• evaluate a distribution of the recurrence period for each customer based on a plurality of multi-dimensional distribution points representing the recurrence period within a range of a point representing predetermined values of the vector strength, the coverage, and the redundancy;
• and
• based on the evaluation of the distribution of the recurrence period for each customer, generating a probability and a recurrence period of the merchant having a recurrent transaction with the customer account using the trained machine learning algorithm based on the generated target label variables.

A non-transitory, tangible computer-readable device having instructions stored thereon that, when executed by at least one computing device, causes the at least one computing device to perform operations comprising:

• storing information gathered from a plurality of data sources in a database, the information comprising merchant identification information of a plurality of merchants and a plurality of transactions of a plurality of customer accounts is gathered for categorizing a transaction recurrency between a merchant of the plurality of merchants and customer account of the plurality of customer accounts;
• aggregating a plurality of sets of transactions based on the gathered information stored in the database, each set of transactions is related to an account-merchant pairing, wherein the account-merchant pairing includes a customer account of the plurality of customer accounts and a merchant of the plurality of merchants;
• splitting each set of the transactions into a plurality of first subset of transactions and corresponding second subset of transactions for generating expanded test data sets, wherein transactions are split based on a split date;
• training a machine learning algorithm by using target label variables, the target label variables are generated based on the second subset of transactions according to predicted transaction dates in the second subset of transactions of the corresponding first subset of transactions of the expanded test data sets and predetermined matching criteria for training a machine learning algorithm, wherein the target label variables identify recurrency of transactions in the first subset of transactions, wherein the matching criteria comprises a number of predictions and a date tolerance, and wherein the predicted transaction dates in the second subset of transactions within the date tolerance are at least the number of predictions in the matching criteria;
• converting transactions dates of the transactions in each first subset of transactions into ordinal dates;
• mapping the ordinal dates onto phase spaces, wherein each phase space of the phase spaces corresponds to one of a weekly, a biweekly, a monthly, a bimonthly, a quarterly, a semi-annually, and a yearly period;
• determining a mean coordinate based on the mapped ordinal dates;
• determining a vector strength, a coverage, and a redundancy, wherein the vector strength, the coverage, and the redundancy are phase variables characterizing a recurrence period;
• predicting the recurrence period for a transaction related to the account-merchant pairing for each customer of the plurality of customers;
• aggregating another set of transactions between the plurality of customers and the merchant, wherein the another set of transactions comprises transactions in the plurality of sets of transactions;
• evaluating a distribution of the recurrence period for each customer based on a plurality of multi-dimensional distribution points representing the recurrence period within a range of a point representing predetermined values of the vector strength, the coverage, and the redundancy;
• and
• based on the evaluation of the distribution of the recurrence period for each customer, generating a probability and the recurrence period of the merchant having a recurrent transaction with the customer account using the trained machine learning algorithm based on the generated target label variables.