As part of business analytics, the Predictive Modeling process is a search for explanatory values. It starts with the identification of a problem that needs to be solved: real estate appraisers want to predict selling price; executives want to predict sales; life insurance carriers want to predict mortality.
A new LIMRA survey found that 60 percent of insurance companies that sell their products through financial professionals use e-signatures and an additional 20 percent plan to add this tool within a year.
The number of people who have had an experience of schizophrenia and are applying for life and disability insurance is small. Those that do represent a high-functioning cohort that are too often tarred with the stereotypes of poor outcome and high rates of suicide associated with the diagnosis. Rarely have these people been seen as individuals or had their risk appropriately assessed.
The life insurance industry has a unique opportunity to outsource its closed blocks of business to support a more efficient cost structure to service the portfolio, to better manage its talent and to enable focus on current and future products.
As modern risk professionals, we pride ourselves on making decisions that are informed by granular risk data, actuarial analysis and complex computer models. Yet, we have discovered that these quantitative elements unfortunately represent only half the decision making process.
All life insurance carriers, for fully-underwritten products, utilize laboratory testing as a key component in assessing an individual’s insurability, regardless of age. Most of these protocols were developed when the average age of applicants and the applied for face amounts were lower than they are today.
Despite many life insurers taking a “backseat approach” to mining large data sets, carriers can profit from big data by using more advanced underwriting analytics and by deepening the relationship with customers, a new report has found.