Eliminating Fraud at the Point of Submission

Eliminating Fraud at the Point of Submission

This post is part of a series sponsored by Insurance Quantified.

Register here to save your spot for the Eliminating Fraud at the Point of Submission Webinar
Date
: Wednesday, September 27, 2023 | 10 am PST / 1 pm EST

Speakers:

Joshua Nolan, Product Manager, Insurance Quantified
Grahm Saunders, Senior Solutions Consultant, SambaSafety

Eliminating Fraud at the Point of Submission

Spotlight on new data and techniques for commercial trucking

Fraudulent insurance submissions are an unfortunate reality. And the manual nature of data validation in underwriting can make it very challenging to detect. What steps can you take to spot inaccurate information early and avoid wasting time on – or worse writing – a bad risk?

Solution: provide your teams with the right data at the right time. The amount of information available to your underwriting teams might seem overwhelming but when paired with the right decision support tools that are built to sort through the mass and surface relevant details, teams are empowered to analyze the output, detect any issues and ultimately make better decisions.

Join Insurance Quantified and SambaSafety for a deep dive webinar on how underwriting teams are using this powerful combination of the right data and tools to discover discrepancies in commercial trucking applications. During this 60-min session, you will learn more about:

The datasets you can use to verify MVRs and match driver rosters
New modeling and analytics that are trained to spot fraud
How and where to leverage decision support tools in the workflow to drive impact

Register here

For More on Insurance Quantified and SambaSaftey:

Press Release

Two Sigma Insurance Quantified and SambaSafety Partner to De-Risk Doctored MVRs for Commercial Auto Underwriting
Read here

See also  Thank you to the judges of the 2023 Insurance Business Australia Awards

Product Brief

SubmissionIQ: The Underwriting Workbench for Teams
View here

Topics
Fraud

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