Composite machine learning system for label prediction and training data collection

A system, comprising:

• one or more processors;
• and
• memory storing one or more instructions that, when executed on the one or more processors, perform a method comprising:
• receiving an electronic request to label a transaction comprising a transaction amount and a descriptive string associated with the transaction;
• generating a set of features associated with the descriptive string;
• determining, via a machine learning model, a label for the transaction based on the set of features;
• providing the label in response to the electronic request;
• receiving user input comprising a reliability indicator related to the label;
• adding a training instance representing the transaction to a set of training data for the machine learning model based on the reliability indicator satisfying a reliability condition;
• and
• updating an aggregate value associated with the label based on the transaction amount.

A method, comprising:

• receiving an electronic request to label a transaction comprising a transaction amount and a descriptive string associated with the transaction;
• identifying a preliminary grouping that is bound to at least one substring of the descriptive string in an associative array;
• determining an accuracy condition is met based on the preliminary grouping;
• comparing the preliminary grouping to a mapping based on the accuracy condition being met;
• using the mapping to determine a label for the transaction;
• providing the label in response to the electronic request;
• receiving user input comprising a reliability indicator related to the label;
• adding a training instance representing the transaction to a set of training data for a machine learning model based on the reliability indicator satisfying a reliability condition;
• and
• updating an aggregate value associated with the label based on the transaction amount.