RMS partners with Microsoft in insurance risk management solutions
Risk modelling and data analytics firm RMS said on Sept. 20 that it partnered with cloud computing platform Microsoft Azure to help insurance companies to overcome limitations to their business agility.
RMS selected Azure as its cloud hosting partner to deliver a next-generation approach to exposure and risk management. This enables RMS clients to access an expansive suite of RMS(one) modelling and analytical tools through a trusted cloud service, used by 90 percent of Fortune 500 firms.
RMS solutions help insurers, financial markets, corporations and public agencies evaluate and manage catastrophe and other insurance risks.
RMS wants to enable re/insurers capitalize on big data and analytics to inject pace into their decision-making, drive greater operational efficiencies in their business and support innovation and sustainable growth in the industry.
“As re/insurers have traditionally faced a myriad of challenges that limit their business agility, access to the immense computational power of RMS(one), combined with Microsoft Azure, will enable them to mine deep into the huge amounts of data they hold to find growth opportunities and minimize potential losses,” said Eric Yau, general manager of software at RMS.
“Their highly secure cloud environment and global footprint ensures that our clients’ data and risk analytics are treated with the utmost security, scalability, and reliability.”
Tony Jacob, managing director, worldwide insurance at Microsoft Corporation added: "RMS is helping drive real digital transformation of the insurance industry. The RMS(one) platform running on Microsoft Azure creates a new class of risk management system for carriers, in a highly protected cloud environment developed with input from regulators worldwide. RMS knows insurance risk and its related data analytics, and Microsoft knows how to provide elastic compute power in the cloud. That combination now powers RMS(one), providing insurance companies with a greater ability to more accurately model and understand their risks.”
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