Clustering-based deviation pattern recognition

A method for clustering-based deviation pattern recognition, comprising:

• obtaining, by a device, first data associated with one or more accounts;
• processing, by the device via a first machine learning model, the first data to obtain clustering information associated with the first data;
• clustering, by the device, the first data into one or more clusters based on the clustering information, wherein the one or more clusters are associated with respective types of deviations from one or more compliance rules indicated by compliance data;
• identifying, by the device and via a second machine learning model, one or more deviation patterns associated with a portion of the first data that is included in a cluster of the one or more clusters,
• wherein the one or more deviation patterns are based on the portion of the first data, the compliance data, and source data associated with the one or more accounts;
• determining, by the device and for the cluster, one or more operations to be performed to mitigate deviations based on the one or more deviation patterns;
• and
• performing, by the device, an action, that is based on the one or more operations, associated with second data grouped into the cluster.

A non-transitory computer-readable medium storing a set of instructions, the set of instructions comprising:

• one or more instructions that, when executed by one or more processors of a device, cause the device to:
• obtain first data associated with one or more accounts, wherein the first data includes at least one of disputed data, compliance rule data, account data, or calculated data;
• process, using a machine learning model, the first data to obtain vector quantization information associated with the first data;
• cluster the first data into one or more clusters based on the vector quantization information, wherein the one or more clusters are indicative of respective types of deviations from at least one of one or more compliance rules indicated by the compliance rule data or one or more calculation rules;
• determine, for a cluster of the one or more clusters, one or more operations to be performed to mitigate the deviation based on a root cause analysis associated with the deviation;
• cluster, based on a first output of the machine learning model, second data into the cluster;
• perform, based on the second data being clustered into the cluster, the one or more operations to the second data to generate modified data;
• and
• transmit, to a server device, the modified data.