Hartford Fire Insurance Compnay

Tracey Ellen Steger

Arthur Paul Drennan, Iii

Qiao Wang

Nicole Rose Sims

Reuben Emory Houser

Kelly J McLaughlin

Vladislav Michael Beznos

Bixia Li

Steven J Cultrera

Saira A Kazmi

Brenda K Boles

Kathleen H Galia

David Gamble

Text input data may be aggregated and mapped to create composite text input data for electronic records. A semantic event may be automatically detected, triggered by a semantic rule and associated semantic tag. The detected semantic event may be flagged, and a text mining result database may be updated by adding an entry to the database. An indication associated with the event may be transmitted, and a back-end application computer server may establish a hierarchy for multiple elements of the electronic records. The computer server may determine a weight variable and response variable for each element in the hierarchy and apply a hierarchical credibility weighting methodology to the elements from level 1, representing the least granular level in the hierarchy, to level n, representing the most granular level in the hierarchy, calculated recursively from level n, to determine a final estimated credibility for the electronic records.

System to determine a credibility weighting for electronic records

In some embodiments, image input data is received from multiple sources. The received image input data may then be aggregated and mapped to create a set of image input data. An event in the set of image input data may be automatically detected, such as by being triggered by a rule and an associated tag. An image mining result database may be updated by adding an entry to the database identifying each detected event and the triggering rule. An indication associated with the image mining result database may then be transmitted to a plurality of risk applications.

System and method for evaluating images to support multiple risk applications

Text input data for an electronic record may be aggregated and mapped to create composite text input data. A semantic event in the composite text input data may be automatically detected, and a text mining result database may be updated by adding an entry identifying the detected semantic event and a triggering semantic rule. An indication of the electronic record may then be transmitted to a back-end application computer server that also determines at least one parameter corresponding to a characteristic of the electronic record. The computer server may then execute a computerized predictive model to predict a future performance characteristic indicator for the electronic record based on the at least one parameter and the indication received from the text mining platform, wherein the future performance characteristic indicator is indicative of a likelihood of an actual value of the electronic record differing from a predicted value of the electronic record.

System to predict future performance characteristic for an electronic record

A device and method is disclosed for automatically grouping data based on characteristics. The device and method include a communication interface for receiving data for component information, a storage medium for storing the received data, and a processor for performing an analysis on the received data to determine parameters included within the data and processing the data using correlations within the data to group or isolate data points. The correlations enable the processor to identify attributes associated with particular groups of the data.

Outlier system for grouping of characteristics

Text input data may be aggregated and mapped to create composite text input data for electronic records. A semantic event may be automatically detected, triggered by a semantic rule and associated semantic tag. The detected semantic event may be flagged, and a text mining result database may be updated by adding an entry to the database. An indication associated with the event may be transmitted, and a back-end application computer server may establish a hierarchy for multiple elements of the electronic records. The computer server may determine a weight variable and response variable for each element in the hierarchy and apply a hierarchical credibility weighting methodology to the elements from level, representing the least granular level in the hierarchy, to level n, representing the most granular level in the hierarchy, calculated recursively from level n, to determine a final estimated credibility for the electronic records.

Text input data for an electronic record may be aggregated and mapped to create composite text input data. A semantic event in the composite text input data may be automatically detected, and a text mining result database may be updated by adding an entry identifying the detected semantic event and a triggering semantic rule. An indication for the electronic record may then be transmitted to a back-end application computer server that also determines at least one parameter corresponding to a characteristic of the electronic record. The computer server may then execute a computerized predictive model to predict a future performance characteristic indicator for the electronic record based on the at least one parameter and the indication received from the text mining platform, wherein the future performance characteristic indicator is indicative of a likelihood of an actual value of the electronic record differing from a predicted value of the electronic record.

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Glastonbury, CT, United States of America

Tracey Ellen Steger