SYSTEM AND METHOD FOR TRANSACTION LEARNING

1 . A method comprising:

receiving, at a categorization system, current transaction data associated with a first financial account, the current transaction data comprising a receipt including a plurality of line items and a total;

receiving, via a network, first demographic data for a first account holder associated with the first financial account;

comparing, via the categorization system, the first demographic data with a second demographic data for a second account holder;

determining, using the categorization system, a first spend category for the current transaction data, the first spend category being identified by the categorization system as a most likely spend category based at least in part upon comparing the first demographic data and the second demographic data;

tagging, using the categorization system, the current transaction data with the first spend category; and

retagging, using the categorization system, at least one line item of the plurality of line items from the first spend category to a different spend category based at least in part on a first request to retag the at least one line item from the first spend category to the different spend category.

2 . The method of claim 1 , further comprising:

determining, using the categorization system, whether the current transaction data has at least a threshold number of similarities with a previous transaction associated with the first financial account; and

comparing, via the categorization system, the current transaction data with transaction data associated with a second account holder,

wherein the first spend category is identified by the categorization system as a most likely spend category further based at least in part upon comparing the current transaction data and the transaction data associated with the second account holder.

3 . The method of claim 2 , wherein the first spend category for the current transaction data is determined based on the threshold number of similarities by comparing merchant data, transaction date, transaction location information, and/or stock-keeping unit level data between the current transaction data and the previous transaction.

4 . The method of claim 2 , wherein the threshold number of similarities between the current transaction data and the previous transaction are based upon transaction location, merchant name, and/or transaction date.

5 . The method of claim 2 , wherein the first demographic data and the second demographic data include at least one of age, gender, income level, household data, type of housing, education data, ancestry, ethnic origin(s), language(s) spoken, employment data, marital status, or dependent data.

6 . The method of claim 2 , wherein the step of determining whether the current transaction data has the threshold number of similarities with the previous transaction comprises:

comparing a first merchant type associated with the current transaction data with a second merchant type for the previous transaction; and

comparing a first time stamp associated with the current transaction data with a second time stamp for the previous transaction.

7 . The method of claim 2 , further comprising:

receiving, at the categorization system, a denial indicating that the first spend category is incorrect; and

transmitting, via the network, a second request for manual input of a new spend category.

8 . The method of claim 2 , wherein each line item of the plurality of line items includes a product or a service and an associated cost for the product or the service.

9 . The method of claim 1 , further comprising:

preparing a visualization report to include the plurality of line items tagged with the first spend category and the total; and

transmitting, via the network, the visualization report to a graphical user interface, wherein the first request to retag is an indication of a user moving the at least one line item to a different area in the graphical user interface while other line items of the plurality of lines items remain in the first spend category.

10 . A system, comprising:

data storage that maintains historical transaction data received from a plurality of financial institutions through a financial institution application programming interface (API); and

a categorization system connected to the data storage, wherein the categorization system comprises a processor and memory storing instructions that, when executed by the processor, causes the processor to:

receive, via a network and from an account holder device, current transaction data associated with a first financial account, the current transaction data comprising a receipt including a plurality of line items and a total;

receive, via the network, first demographic data for a first account holder associated with the first financial account;

compare the first demographic data with a second demographic data for a second account holder;

determine a first spend category for the current transaction data, the first spend category being identified by the categorization system as a most likely spend category based at least in part upon comparing the first demographic data and the second demographic data;

tag the current transaction data with the first spend category; and

retag at least one line item of the plurality of line items from the first spend category to a different spend category based at least in part on a first request to retag the at least one line item from the first spend category to the different spend category.

11 . The system of claim 10 , wherein the instructions, when executed by the processor, further cause the processor to:

determine whether the current transaction data has a threshold number of similarities with the historical transaction data for the first financial account by comparing the current transaction data with the historical transaction data for the first financial account; and

compare the current transaction data with transaction data associated with the second account holder,

wherein the first spend category is identified by the categorization system as a most likely spend category further based at least in part upon comparing the current transaction data and the transaction data associated with the second account holder.

12 . The system of claim 11 , wherein the instructions, when executed by the processor, further cause the processor to determine the threshold number of similarities by comparing merchant data, transaction date, transaction location information, and/or stock-keeping unit level data between the current transaction data and the historical transaction data.

13 . The system of claim 11 , wherein the threshold number of similarities between the current transaction data and the historical transaction data are based upon transaction location, merchant name, and/or transaction date.

14 . The system of claim 11 , wherein the first demographic data and the second demographic data include at least one of age, gender, income level, household data, type of housing, education data, ancestry, ethnic origin(s), language(s) spoken, employment data, marital status, or dependent data.

15 . The system of claim 11 , wherein the instructions, when executed by the processor, further cause the processor to determine whether the current transaction data has the threshold number of similarities with the historical transaction data by:

comparing a first merchant type associated with the current transaction data with a second merchant type included in the historical transaction data; and

comparing a first time stamp associated with the current transaction data with a second time stamp included in the historical transaction data.

16 . The system of claim 11 , wherein the instructions, when executed by the processor, further cause the processor to receive the first demographic data for the first account holder when a number of similarities between the historical transaction data and the current transaction data is below the threshold number of similarities.

17 . A system comprising:

a categorization system comprising a first processor and a first memory storing first instructions that, when executed by the first processor, causes the first processor to:

receive, via a network, current transaction data associated with a first financial account, the current transaction data comprising a receipt including a plurality of line items and a total;

receive, via the network, first demographic data for a first account holder associated with the first financial account;

compare the first demographic data with a second demographic data for a second account holder;

determine a first spend category for the current transaction data, the first spend category being identified by the categorization system as a most likely spend category based at least in part upon comparing the first demographic data and the second demographic data;

retag at least one line item of the plurality of line items from the first spend category to a different spend category based at least in part on a first request to retag the at least one line item from the first spend category to the different spend category; and

a computing device operating an application and configured to:

receive, at an input/output device, an indication that a user wishes to retag the at least one line item of the plurality of line items from the first spend category to the different spend category; and

transmit, via the network, a first request to retag the at least one line item of the plurality of line items on the receipt from the first spend category to the different spend category.

18 . The system of claim 17 , wherein the first instructions, when executed by the first processor, further cause the first processor to:

determine whether the current transaction data has a threshold number of similarities with historical transaction data for the first financial account by comparing the current transaction data with the historical transaction data for the first financial account; and

compare the current transaction data with transaction data associated with the second account holder,

wherein the first spend category is identified by the categorization system as a most likely spend category further based at least in part upon comparing the current transaction data and the transaction data associated with the second account holder.

19 . The system of claim 18 , wherein the first instructions, when executed by the first processor, further cause the first processor to determine the threshold number of similarities by comparing merchant data, transaction date, transaction location information, and/or stock-keeping unit level data between the current transaction data and the historical transaction data.

20 . The system of claim 18 , wherein the first instructions, when executed by the first processor, cause the first processor to receive, via the network, the first demographic data for the first account holder associated with the first financial account when a number of similarities between the historical transaction data and the current transaction data is below the threshold number of similarities.