Risk Management (General)
To Tell the Truth: Applicant Nondisclosure of Obesity and HIV and Hepatitis C
Applicants’ self-disclosed Human Immunodeficiency Virus (HIV) and hepatitis C virus (HCV) status as well as body mass index (BMI) are important risk factors for morbidity and mortality in both accelerated and traditional (full) underwriting processes. In combination with self-disclosed smoking and medical history (diabetes, hypertension, heart failure, high cholesterol), which was discussed in a previous issue of Contingencies, these conditions constitute a majority of the leading medical inputs to the risk-assessment process.
Seasonal Influenza and Mortality
Can big data from a bad flu season yield insights into all-cause mortality and help guide insurance risk assessment? RGA and Blue Dot partner to explore the global spread of infectious disease and arrive at fascinating insights.
Impaired Risk Review: Rage Against the Machine
Increasingly, computer templates are making binding underwriting decisions through information that is programmed in with final decisions virtually unappealable. It’s a frustrating situation that is taking the human element out of a very human situation of health and risk assessment.
Classification Model Performance (Gen Re Risk Insights)
Insurers are increasingly developing prediction models to use in their insurance processes. Often these models are using traditional techniques, but more and more we see machine learning techniques being applied.
Underwriting and Calculators
On our travels we speak to a lot of people about underwriting manual developments. One topic which comes up time and again is calculators: great invention or the work of The Devil?
Cancer Risks: Are We Getting Them Right?
Researching cancer mortality over the past few months has proved to be a bit of an eye-opener, and in three ways: firstly the level of excess mortality seen in a number of cancers, secondly the duration over which an extra risk persists, and thirdly that excess mortality may extend over a considerable period.
Can Big Data Spell the End of Uncertainty?
Big data applications are increasingly shaping our everyday lives, making trends more transparent and patterns more predictable. Like insurance, science, notably medicine, is equally subject to the novel possibilities and demands resulting from big data. Will risks gradually decline as trends and patterns become more predictable in healthcare and for behavior? Are the days of insurance and science as we know them numbered?
Big Data, Big Insight – What Does It Offer to Life Insurers?
In an earlier Risk Insights article, we examined the bold claim for big data that the abundance and speed of information would make knowledge redundant. We concluded that if knowledge is understood as the body of causal explanations for observed statistical correlations, there could indeed be a grain of truth in such a claim, especially in the field of commercial applications.
Insuring HIV - Q&A with Dr. Dan Zimmerman
Recently, RGA’s Dr. Dan Zimmerman spoke to the Association of Home Office Underwriters (AHOU) about the history of HIV/AIDS and insurance, and how risk modeling can reasonably conclude that some individuals with HIV can now be candidates for life insurance coverage. We sat down with Dr. Zimmerman to discuss his presentation and the paradigm shift in the availability of life insurance for people with HIV.
Impaired Risk Review: Crediting Systems
Once upon a time, being a standard risk in the underwriting process was the best you could hope for. It meant that your health was at the top of the group of insured lives being considered and eligible for the best rate, and that you would live all the way out to the prediction of the actuarial life tables. Now, life insurers have created a whole tier of preferred and super preferred pricing that makes a standard issue almost seem like a rated policy.