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Webinar: A New Threat to Auto Fraudsters; Leveraging Automated Blackbox-to-Cloud Data Analytics
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This 1 hour webinar is being presented by Jason Bayley, CEO, Collision Sciences and Chad Zinn, Chief Sales Officer, Collision Sciences.

4/9/2019
When: 2:00 pm CT
Where: Online Webinar
N83 W13410 Leon Road
Menomonee Falls, Wisconsin  53051
United States
Contact: Greg Haag
414 375-2992 ext. 1118


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Join Jason Bayley and Chad Zinn from Collision Sciences as they walk us through some of the tools and techniques they have incorporated into their cloud-based diagnostic tool and data analytics platform.  The end-to-end solution uses a mobile app and pocket-sized car adapter to simplify the access and interpretation of crucial data from Event Data Recorders (EDRs).

Learn about the exciting future of EDR data, how cloud analytics are now being utilized to fully automate fraud alerts and reports at scale, new flags and referrals that can be generated, and the use of aggregated research and data sources being used to reduce loss from insurance fraud.

We’ll end the webinar with a brief demonstration of the affordable Android app-based EDR tool and get an inside look at the automated Recon reports, flags and contextualized data.  Find the answers to all the questions you have about implementing an EDR fraud detection technology program and learn about how to transform an under-utilized data source into an “internal” data source.     

Learning Points:
• Brief history and the exciting future of automated analytics for Event Data Recorder (EDR) data, i.e. "BlackBox" data
• Suspicious Alerts that your team may not be checking for, which can now be automated at scale
• How you can automate cross-referencing of reported circumstances to proactively generate SIU referrals and reports 

• New tools & techniques for affordable, scaled, cloud-enabled Crash Data Retrieval
• Examples of automated contextualization and big data analysis for case-specific EDR data 
• Instant EDR-based Reconstruction and 1st/3rd Party Injury Probability Reports 

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