Detecting and correcting outliers in categories of transactions

A system, comprising:

• a memory including computer-executable instructions;
• and
• a processor configured to execute the computer-executable instructions and cause the system to:
• receive electronic transaction data indicative of one or more current transactions, wherein the one or more current transactions are associated with a user of a software application;
• identify, for each transaction of the one or more transactions, a category using a first machine learning model;
• compute, for the user, a distribution for each category of a plurality of categories, wherein each category of the plurality of categories has also been assigned to a plurality of other transactions by other users of the software application;
• identify, using a second machine learning model, a particular category of the plurality of categories as an anomalous category for the user, based on the distribution computed for the user for the particular category and corresponding distributions for the other users for the particular category;
• and
• update a category assigned to at least one of the one or more transactions such that a delta between a value for the user relating to the anomalous category and corresponding values for the other users relating to the particular category is reduced.

A non-transitory computer readable medium comprising instructions to be executed in a computer system, wherein the instructions when executed in the computer system cause the computer system to:

• receive electronic transaction data indicative of one or more current transactions, wherein the one or more current transactions are associated with a user of a software application;
• identify, for each transaction of the one or more transactions, a category using a first machine learning model;
• compute, for the user, a distribution for each category of a plurality of categories, wherein each category of the plurality of categories has also been assigned to a plurality of other transactions by other users of the software application;
• identify, using a second machine learning model, a particular category of the plurality of categories as an anomalous category for the user, based on the distribution computed for the user for the particular category and corresponding distributions for the other users for the particular category;
• and
• update a category assigned to at least one of the one or more transactions such that a delta between a value for the user relating to the anomalous category and corresponding values for the other users relating to the particular category is reduced.