Who is this product for:
Benefits
Generate accurate & personalized projections
Aivante’s patent-pending analytics engines analyzes the massive “All Payers” healthcare claims databases and more than 1,000 health plan coverage variables. It then applies machine learning methods against the data to understand how medical spending is likely to vary based on an individual’s geographic location and their health condition, including how their health conditions are likely to progress over time. The result is highly accurate long-term predictions of medical and long-term care expenses, which can be presented in aggregate and disaggregated form (eg, premiums and out-of-pocket expenses by year). This personalized, “bottoms-up” approach is superior to traditional actuarial methods that use “top-down” methods.
Optimize Medicare elections
Compare total lifetime premiums and out-of-pocket expenses based on different Medicare elections, such as Part D, Part C and Part F, and choose the Medicare elections that are likely to yield the lowest lifetime costs, given the individual’s health condition.
API solutions
Build your own solution for advisors or consumers. Pass basic NPI data to Aivante and receive annual expenses, decomposed by type and year. Use the data to power financial planning applications for advisors or simple tools for wealth management client websites.
Improve financial planning, drive more savings & frame product solutions
The predictive data can be used to frame savings goals. For instance, a client’s retirement medical expenses might total $325,000, but have a present value of $185,000 at retirement, which can be framed as a goal to fund by retirement, perhaps by saving more and using Health Savings Accounts. Similarly, long-term care expenses can be framed as a separate savings goal by retirement, or can frame the need for a hybrid long-term care / life insurance product.
Cloud solutions
A co-branded solution is available for advisor use that quickly generates a personalized analysis for clients, as a PDF report.