State Farm Mutual Automobile Insurance Company

Justin Devore

Timothy Joel Davis

Amy Engelhorn

Dijon Schroff

Joanna Salata

Sarah Kirby

Jason Beckman

Garren King

Douglas Albert Graff

Ronald R Duehr

Shane Tomlinson

Gary Foreman

John Westhues

Carlee A Clymer

Stephen E Wilson

Yohan Santos

Bradley J Walder

David A Pluimer

Denson Smith

Tracey Leigh Knorr

Erin E Miller

Joshua T Monk

Pavan Kumar Bhagavatula

Aaron Ames

Andrew Mathes

John G McConkey

Michael Cicilio Fresquez

Himanshu Chhita

Michele Wittman

Alexis Danielle Cates

Stephen Young

Vincent M Hummel

Rajesh Panicker

Carrie A Read

Michael Brown

Chad Mychal Hirst

Robin A Rose

Holly Kay Sanderson

Merril Myers

Sateesh K Nallamothu

Dayne Dalpoas

Jennifer Megargell

Ruth Anne Grosskopf

Barbara Williford

Mehron Beheshti

Alexandria Pokorny

Andy Pulkstenis

Matthew Marsh

Jeremy David Dickerson

Kathleen Cordiner

A property analyzer device may include (1) a receiver configured to receive property identification information from a mobile device, the received property identification information including a picture of the property and/or a property location; (2) a retriever configured to retrieve property-related information from multiple property sources (such as public record databases, websites, or an internal company database) based upon the received property identification information; (3) a processor configured to consolidate the retrieved property-related information into a consolidated property-related image of the property; and (4) a transmitter configured to transmit the consolidated property-related image to the mobile device for review by a user. The mobile device may display the consolidated images that may include several images of the property taken over time (such as showing repairs or improvements made), home insurance quotes, home loan quotes, and other types of property-related or community information that home buyers or owners would find useful.

Systems and methods for analyzing property related information

Techniques described herein relate to training and executing machine learning models configured to determine injury probabilities based on vehicle accident data. In some cases, a decision tree model may be constructed including various branching criteria based on particular vehicle accident data and injury ground truth data, including leaf nodes storing corresponding injury probabilities. A model execution component may execute the trained models to determine the probability of potential injuries associated with vehicle accidents. Based on the potential injury probabilities, a vehicle accident analysis system may identify target computer systems and/or target processes to be initiated on the target systems, based on the injury probabilities. In some example, the model execution architecture may be implemented using an event-driven system and/or cloud-based data storage and services to receive, store, and process data events associated with individual vehicle accidents.

Machine learning models for vehicle accident potential injury detection

Variants of an application, such as user interface variants and/or logic variants, can be used in a production environment as part of a designed experiment. An experiment manager can cause the application to operate based on different variants for different users and/or instances of input data. Synchronous and/or asynchronous results can be collected that indicate impacts of the variants at the application and/or other downstream systems. Such asynchronous and/or synchronous results can be used to prove or disprove a hypothesis associated with the designed experiment.

Designed experiments for application variants

A method of identifying a vehicle total loss claim includes retrieving a plurality of historical vehicle records, labeling the records as repaired or total loss, calculating mean cost values, training a regression model, optimizing a probability threshold, analyzing a plurality of inputs to generate a prediction, and transmitting the prediction. A computing system includes a transceiver; a processor; and a memory storing instructions that, when executed by the processor, cause the computing system to receive answers, transmit the answers, receive a prediction, when the prediction is repairable, generate a repair suggestion, and when the prediction is total loss, generate a settlement offer. A non-transitory computer readable medium containing program instructions that when executed, cause a computer to receive answers, transmit the answers, receive a prediction, when the prediction is repairable, generate a repair suggestion, and when prediction is total loss, generate a settlement offer.

Method and system for early identification and settlement of total loss claims

Candidate function data can include data for multiple candidate functions that may indicate whether insurance claims are likely to involve certain issues, such as comparative negligence issues. A real-time claim identifier can use the candidate function data to determine, in near real-time, if claim data for a new insurance claim submitted to an insurance company satisfies at least one of the candidate functions. If the claim data satisfies at least one of the candidate functions, the insurance claim can be assigned to a corresponding destination in the insurance company, such as a department that processes comparative negligence claims, for further processing.

Claim analysis based on candidate functions

During a communication session with a caller, a representative can enter information into a claim loss reporting tool to generate a loss report associated with an insurance claim. A dialogue advisor can cause the claim loss reporting tool to suggest questions that the representative should ask, and/or actions the representative should take, during the communication session. The dialogue advisor can make such suggestions, during the communication session, based at least in part on confidence levels of simulated destination predictions that correspond with destination predictions a claim router would make according to the same information associated with the loss report.

Dialogue advisor for claim loss reporting tool

Systems and method for analyzing property related information

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Bloomington, IL, United States of America

Justin Devore