High impact automation. All the way from first notice of loss to the claim resolution.
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AmTrust Financial Services CIO and COO, Ariel Gorelik shares their transformational story of leveraging AI technology with omni:us – and achieving 75% automation of incoming claims information getting attached to existing claims files.
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omni:us data-driven, modular tools for insurers which automate data-intense workflows – empowering your claims teams to act with greater accuracy and care. Unlock an AI-powered and data-driven claims journey and supercharge your insurance business for the future.
The omni:us platform allows you to quickly deploy AI into your claims processes.
Gather all relevant information and generate structured data from policy and claims documents.
Combine claims domain knowledge with validated data to automate decisions.
Automate processes with AI-powered decision support along the claims value chain.
omni:us acts as an interface between insurance core systems, third-party solutions, and inbound customer communications. Our AI product acts as a smart link between these otherwise isolated systems. By uniting them, you capture true business value.
Straight through processing in 4 essential steps. Supercharge your claims process, from FNOL to resolution.
Our mission is to enhance insurance industry processes and workflows by simplifying the claims handling life cycles. omni:us understands the value of automation by identifying key processes to shorten cycle times, enhance customer expectations, increase employee satisfaction, and improve data integrity to create better outcomes in the overall claims handling process.
ISO/IEC 27001 :13
omni:us is proud to be one of Europe‘s Horizon2020 SME-Instrument and Fet Flag Champions.
We are grateful to have received funding from the European Union’s Horizon 2020 Research and Innovation Programme under Grant Agreement No 820323; 850053.
Co-financed by European Fund for Regional Development (EFRE)
Pro Fit-Project “Vollautomatisierung der Wertschöpfungskette im Digitalisierungsprozess von Archivdaten” with support of IBB/EFRE in 2016/2017.
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