Machine Learning Systems and Methods for Property Estimation Anomaly Detection

Abstract

Machine learning systems and methods for property estimate anomaly detection are provided. The system receives property estimation data from a data source and processes the property estimation data to extract line item information from the property estimation data. The line item information, along with majority estimate information, is then processed by an automated anomaly detection process which performs majority estimate unit detection, line item quantity detection, and line item cluster detection on the extracted line item information using a plurality of machine learning models. The system then processes the majority estimate unit detection, line item quantity detection, and line item cluster detection to identify anomalous data in the line item information, and generates and displays a summary of the anomalous data in a graphical user interface screen of a claims estimation software application.

Dependencies are inferred from the claim text.