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 clustering machine learning model, the first data to obtain clustering information associated with the first data;
• clustering, by the device and via the clustering machine learning model, the first data into one or more clusters based on the clustering information, wherein the one or more clusters associated with respective types of deviations from one or more compliance rules indicated by compliance data, wherein the one or more compliance rules define how the first data is to be reported to a credit reporting agency;
• identifying, by the device and via a pattern recognition 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, and
• wherein data from two or more different data sources that utilize two or more different data formatting conventions is input to the pattern recognition machine learning model, wherein the first data includes the data from the two or more different data sources;
• clustering, based on an output of the clustering machine learning model, second data into the cluster,
• wherein the output of the clustering machine learning model is provided by a lesser quantity of user interfaces than a quantity of user interfaces for individually presenting the data from the two or more different data sources;
• determining, by the device and for the cluster, one or more corrective operations to be performed to mitigate the deviations based on the one or more deviation patterns;
• performing, by the device, an action, that is based on the one or more corrective operations, associated with the second data;
• and
• transmitting, by the device and to a device of the credit reporting agency, modified second data, wherein the modified second data is in accordance with the one or more compliance rules.

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 clustering machine learning model, the first data to obtain vector quantization information associated with the first data;
• cluster, using the clustering machine learning model, 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, wherein the one or more compliance rules define how the first data is to be reported to a credit reporting agency;
• determine, using a pattern recognition machine learning model, for a cluster of the one or more clusters, one or more corrective operations to be performed to mitigate a deviation based on a root cause analysis associated with the deviation,
• wherein data from two or more different data sources that utilize two or more different data formatting conventions is input to the pattern recognition machine learning model, wherein the first data includes the data from the two or more different data sources;
• cluster, based on a first output of the clustering machine learning model, second data into the cluster,
• wherein the first output of the clustering machine learning model is provided by a lesser quantity of user interfaces than a quantity of user interfaces for individually presenting the data from the two or more different data sources;
• perform, based on the second data being clustered into the cluster, the one or more corrective operations to the second data to generate modified second data;
• and
• transmit, to a server device of the credit reporting agency, the modified second data, wherein the modified second data is in accordance with the one or more compliance rules.