DATA AGGREGATION FOR THRESHOLDING-BASED ACCOUNT MANAGEMENT
Aspects of the subject technology allow an entity to aggregate transaction data for safeguarding. Aspects include obtaining a set of data items associated with a set of transactions, and, for each respective data item, determining a funding type corresponding to the respective data item based at least in part on a respective attribute and augmenting the respective data item based on the determined funding type and an attribute estimation. Aspects also include aggregating the set of data items into respective groups based on the funding type, the merchant identifier, and/or the jurisdiction identifier, and transmitting a respective group to a service for determining whether a respective bank account includes a threshold amount of funds based on the amounts of data items in the respective group.
1 . A method comprising:
obtaining a set of data items associated with a set of transactions, wherein each respective data item includes an amount and one or more respective attributes, the one or more respective attributes comprising a merchant identifier and a jurisdiction identifier;
for each respective data item:
determining a funding type corresponding to the respective data item based at least in part on the one or more respective attributes; and
augmenting the respective data item based on the determined funding type and one or more attribute estimations corresponding to the one or more respective attributes;
aggregating the set of data items into respective groups based at least in part on the funding type, the merchant identifier, and the jurisdiction identifier; and
transmitting at least one of the respective groups to a service for determining whether a respective bank account includes a threshold amount of funds based on the amounts of data items in the at least one of the respective groups.
2 . The method of claim 1 , wherein augmenting the respective data item with the one or more attribute estimations comprises:
identifying one or more incomplete attributes of the one or more respective attributes of the respective data item;
obtaining the one or more attribute estimations for the one or more incomplete attributes; and
adjusting the one or more incomplete attributes based at least in part on the one or more attribute estimations.
3 . The method of claim 1 , wherein the one or more attribute estimations are obtained from a machine learning model, wherein the machine learning model is trained based on historical data items.
4 . The method of claim 1 , wherein the one or more respective attributes of a respective data item include a settlement date and the one or more attribute estimations include an estimated settlement date.
5 . The method of claim 1 , wherein the set of data items are obtained as a batch and the method is performed on a periodic basis.
6 . The method of claim 1 , wherein the set of data items are obtained as a stream and the method is performed as each data item is obtained.
7 . The method of claim 1 , wherein transmitting the at least one of the respective groups occurs at a predetermined time of day.
8 . The method of claim 1 , further comprising:
obtaining reconciliation data associated with a data item of the set of data items; and
updating the one or more attribute estimations of the data item based at least in part on the reconciliation data.
9 . The method of claim 1 , wherein the funding type indicates that the respective data item is associated with e-money funds.
10 . The method of claim 1 , wherein the one or more respective attributes further comprises a currency identifier, and the set of data items are aggregated into respective groups further based on the currency identifier.
11 . The method of claim 1 , wherein transmitting the at least one of the respective groups comprises transmitting a sum of the amounts of the data items in the at least one of the respective groups.
12 . An electronic device comprising:
a memory; and
a processor configured to:
obtain a set of data items associated with a set of transactions, wherein each respective data item includes an amount and one or more respective attributes, the one or more respective attributes comprising a merchant identifier and a jurisdiction identifier;
for each respective data item:
determine a funding type corresponding to the respective data item based at least in part on the one or more respective attributes; and
augment the respective data item based on the determined funding type and one or more attribute estimations corresponding to the one or more respective attributes;
aggregate the set of data items into respective groups based at least in part on the funding type, the merchant identifier, and the jurisdiction identifier; and
transmit at least one of the respective groups to a service for determining whether a respective bank account includes a threshold amount of funds based on the amounts of data items in the at least one of the respective groups.
13 . The electronic device of claim 12 , wherein the processor is configured to augment the respective data item with the one or more attribute estimations by:
identifying one or more incomplete attributes of the one or more respective attributes of the respective data item;
obtaining the one or more attribute estimations for the one or more incomplete attributes; and
adjusting the one or more incomplete attributes based at least in part on the one or more attribute estimations.
14 . The electronic device of claim 12 , wherein the one or more attribute estimations are obtained from a machine learning model, wherein the machine learning model is trained based on historical data items.
15 . The electronic device of claim 12 , wherein the one or more respective attributes of a respective data item include a settlement date and the one or more attribute estimations include an estimated settlement date.
16 . The electronic device of claim 12 , wherein the set of data items are obtained as a batch and the obtaining, determining, augmenting, aggregating, and transmitting steps are performed on a periodic basis.
17 . The electronic device of claim 12 , wherein the set of data items are obtained as a stream and the obtaining, determining, augmenting, aggregating, and transmitting steps are performed as each data item is obtained.
18 . The electronic device of claim 12 , wherein transmitting the at least one of the respective groups occurs at a predetermined time of day.
19 . The electronic device of claim 12 , wherein the processor is further configured to:
obtain reconciliation data associated with a data item of the set of data items; and
update the one or more attribute estimations of the data item based at least in part on the reconciliation data.
20 . A non-transitory computer-readable medium comprising:
computer-readable instructions that, when executed by a processor, cause the processor to perform one or more operations comprising:
obtaining a set of data items associated with a set of transactions, wherein each respective data item includes an amount and one or more respective attributes, the one or more respective attributes comprising a merchant identifier and a jurisdiction identifier;
for each respective data item:
determining a funding type corresponding to the respective data item based at least in part on the one or more respective attributes; and
augmenting the respective data item based on the determined funding type and one or more attribute estimations corresponding to the one or more respective attributes;
aggregating the set of data items into respective groups based at least in part on the funding type, the merchant identifier, and the jurisdiction identifier, and
transmitting at least one of the respective groups to a service for determining whether a respective bank account includes a threshold amount of funds based on the amounts of data items in the at least one of the respective groups.