Artificial intelligence system for anomaly detection in transaction data sets

A method to detect anomalies in transaction data sets, the method comprising:

• receiving a first data set for training a first machine learning model to detect anomalies in the transaction data sets using a machine learning technique;
• accessing at least one micro-model previously trained using at least one second data set separate from the first data set;
• determining risk scores from the first data set using the at least one micro-model;
• enriching the first data set with the risk scores;
• and
• determining the first machine learning model for the enriched first data set using the machine learning technique.

A non-transitory computer-readable medium having stored thereon computer-readable instructions executable to detect anomalies in transaction data sets based on one or more machine learning models, the computer-readable instructions executable to perform modeling operations which comprises:

• receiving a first data set for training a first machine learning model to detect anomalies in the transaction data sets using a machine learning technique;
• accessing at least one micro-model previously trained using at least one second data set separate from the first data set, wherein the first data set and each of the at least one second data set comprise segregated data sets for a federated training system;
• determining risk scores from the first data set using the at least one micro-model;
• enriching the first data set with the risk scores;
• determining the first machine learning model for the enriched first data set using the machine learning technique;
• determining a second machine learning model for the first data set using the machine learning technique, wherein the second machine learning model is for an unenriched data set corresponding to the first data set;
• and
• generating a model explanation output based on the first machine learning model and the second machine learning model, wherein the model explanation output comprises a comparison between each feature of each classification task within the first machine learning model and the second machine learning model.