A system and method a method for providing for providing personalized transaction learning and tagging. The method may include tagging transactions associated with one or more financial accounts belonging to an account holder, whether the account holder be the primary, secondary, or a related account holder, such as a spouse, parent, guardian, and the like. The method may include linking all accounts belong to and/or associated with an account holder and receiving transaction data from each linked account, including, for example, transaction date, transaction time, transaction amount, merchant name, merchant location, merchant identifier, account number used in transaction, SKU-level transaction information, and/or other purchase identifiers (e.g., merchant-provided product/service name, account holder-provided product/service name, and the like). Once the system receives the transaction data, the system may query the account holder for input regarding the transaction data. The input may include tagging the transaction as belonging to a particular spending category and/or affirming or denying that the transaction belongs to a particular category. The system may receive and create categories based on account holder data, demographic data, credit data, and account holder profile data.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. 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); a categorization system connected to the data storage, wherein the categorization system: receives, via a network from an account holder device, at least one category associated with at least one transaction in the historical transaction data; receives, via a network, account holder profile data and demographic data; receives, via a financial institution API, current transaction data; determines, using a categorization subsystem, whether the current transaction data has a threshold number of similarities with a transaction in the historical transaction data by comparing the current transaction data with the historical transaction data; determines, using machine learning techniques implemented by the categorization subsystem, a most-likely spend category based on the threshold number of similarities; receives, via a network from the account holder device; a confirmation that the most-likely spend category is correct; tags, using the categorization subsystem, the current transaction data with most-likely spend category; stores, in the data storage, the current transaction data along with the tagged spend category; and prepares a visualization report to include all transaction data tagged with the most-likely spend category. 2. The system of claim 1, wherein the account holder profile data includes account holder name, address, telephone number, email address, account number(s), secondary account holders associated with each account, related account(s), account transaction data, account status, and/or spend categories that are used to tag transactions made by the account holder 3. The system of claim 1, wherein the demographic data includes age, gender, income level, household data, type of housing, education data, ancestry and/or ethnic origin(s), language(s) spoken, employment data, marital status, and/or dependent data. 4. The system of claim 1, wherein to determine whether the current transaction data has a threshold number of similarities with a transaction in the historical transaction data, the categorization system compares a merchant type associated with the current transaction data with merchant types for each respective merchant in the historical transaction data. 5. The system of claim 4, wherein to determine whether the current transaction data has a threshold number of similarities with a transaction in the historical transaction data, the categorization subsystem compares a time stamp associated with the current transaction data with a time stamp for each respective transaction in the historical transaction data. 6. The system of claim 1, wherein, to tag the current transaction data, the transaction subsystem indicates the most-likely spend category within a transaction category of a database in the data storage that maintains the current transaction data. 7. The system of claim 1, wherein the most-likely spend category is customizable via inputs from the account holder device. 8. The system of claim 1, wherein the categorization system, using the categorization subsystem further implements machine learning techniques to identify additional transactions among the historical transaction data to be associated with the most-likely spend category. 9. The system of claim 8, wherein the categorization system receives, via a network from the account holder device, a response indicating whether the additional transactions are associated with the most-likely spend category. 10. The system of claim 1, wherein the data storage and categorization system are components of a financial institution backend system. 11. A system for a computer-implemented method, comprising: data storage that maintains transaction data received from a plurality of financial institutions through a financial institution application programming interface (API); a categorization system connected to the data storage, wherein the categorization system: receives, via a network from an account holder device, at least one category associated with at least one transaction in the historical transaction data; receives, via a network, account holder profile data and demographic data; receives, via a financial institution API, current transaction data; determines, using a categorization subsystem, whether the current transaction data has a threshold number of similarities with a transaction in the historical transaction data by comparing the current transaction data with the historical transaction data; determines, using machine learning techniques implemented by the categorization subsystem, a proposed spend category based on the threshold number of similarities, the demographic data, and the account holder profile data; receives, via a network from an account holder device; a confirmation that the proposed spend category is correct; tags, using a categorization subsystem, the current transaction data with proposed spend category; stores, in the data storage, the current transaction data along with the tagged spend category; and prepares a visualization report to include all transaction data tagged with the proposed spend category. 12. The system of claim 11, wherein the account holder profile data includes account holder name, address, telephone number, email address, account number(s), secondary account holders associated with each account, related account(s), account transaction data, account status, and/or spend categories that are used to tag transactions made by the account holder 13. The system of claim 11, wherein the demographic data includes age, gender, income level, household data, type of housing, education data, ancestry and/or ethnic origin(s), language(s) spoken, employment data, marital status, and/or dependent data. 14. The system of claim 11, wherein to determine whether the current transaction data has a threshold number of similarities with a transaction in the historical transaction data, the categorization system compares a merchant type associated with the current transaction data with merchant types for each respective merchant in the historical transaction data. 15. The system of claim 14, wherein to determine whether the current transaction data has a threshold number of similarities with a transaction in the historical transaction data, the categorization subsystem compares a time stamp associated with the current transaction data with a time stamp for each respective transaction in the historical transaction data. 16. The system of claim 11, wherein, to tag the current transaction data, the transaction subsystem indicates the spend category within a transaction category of a database in the data storage that maintains the current transaction data. 17. The system of claim 11, wherein the proposed spend category is customizable via inputs from the account holder device. 18. The system of claim 11, wherein the categorization system, using the categorization subsystem further implements machine learning techniques to identify additional transactions among the historical transaction data to be associated with the proposed spend category. 19. The system of claim 18, wherein the categorization system receives, via a network from the account holder device, a response indicating whether the additional transactions are associated with the proposed spend category. 20. The system of claim 11, wherein the data storage and categorization system are components of a financial institution backend system.