Automatic alert dispositioning using artificial intelligence

Abstract

A system for identifying false positives about a user and closing resulting alerts before the alert consumes system resources may use machine learning techniques including clustering and multi-labeling classification to effectively categorize prior text-based notes. The categorized notes may be used to identify and automatically close false positive alerts. Moreover, weak labeling, AI transformers/sentence transformation, and/or k-means cluster analysis may be used to condense large quantities of textual data into ML model components with improved interpretability. Customer relationship management (CRM) platform and Risk Management Supervision (RMS) note analysis captures past work and leverages it to reduce redundancies in future expert user/supervisory/customer advisory efforts. The various ML techniques disclosed herein output numerical features for improved alert triaging.