Princeton, NJ – September 2022 — Claros Analytics, the healthcare industry’s leading actuarial software firm, has developed a new web-based application that simplifies designing and pricing employer health benefit plans.
ClarosPlan guides users through the process of defining a plan and determining the impact of changes on overall plan cost. The application can assess a wide breadth of potential plan changes, including employee demographic changes, changes to the medical and Rx plan design, an increase or decrease to the stop loss deductible, changes to network pricing including reference-based pricing, and is a unique resource for comprehensive plan analysis and forecasting.
ClarosPlan can be accessed from internet browsers running on a variety of devices and operating systems, simplifying deployment. The user-friendly interface includes error control, allowing for quicker and easier plan design inputs by its users. All of Claros Analytics’ applications are built on our highly accurate predictive engine that generates actuarial projections using specialized claims curves and Monte Carlo simulations, rather than the table-driven methodology of legacy tools.
“ClarosPlan empowers our users to deliver powerful analysis, keen insights, and credible projections of the dynamics of the self-insured plan,” says Todd Owen, Claros Analytics CEO.
Industry professionals can take advantage of this user-friendly software to easily compare and price plans, anticipate large claims, advise clients on switching from a fully-insured to self-funded plan, and enjoy a competitive advantage over those using outdated, time-consuming methods for plan designs.
Contact sales@clarosanalytics.com to schedule a demo.
About Claros Analytics
Claros Analytics builds next-generation analytical software applications to model, price, and predict health benefits costs. Clients, including stop loss carriers, underwriters, reinsurers, benefits consultants and advisors, plan administrators, and plan sponsors, achieve a competitive advantage by using our predictive analytics to model health plan changes, define self-funded opportunities, and budget self-funded plan costs.
