Risk Management (General)
Don’t Share Your Health Data with Insurance Companies Just for the Perks
Insurers are today capable of and are, in fact, gathering ever-more-detailed information about us, using publicly available and purchasable information like shopping records, household details, and social-media profiles to inform decisions.
The Risk of Anti-Selection in Protection Business from Advances in Statistical Genetics
Four-part webcast series presents new research and explores potential implications for the insurance industry.
Why Data Veracity Will Reshape Life Insurance
From its earliest days, life insurance has been fueled by data. Today, the industry is more data-driven than ever. Life insurers rely on data to make better operational, risk and pricing decisions. They use data to develop new products and business models. Increasingly, they leverage data to incentivize customers to reduce their exposure to risks and help them avoid incurring losses.
Travel Risks & Life Underwriting
Conditions within a country can change rapidly at any given time. When assessing and classifying an applicant’s travel related risk, it is important for underwriters to review the relevant U.S. Department of State Travel Advisory information in its entirety.
Why ‘Big Data’ Will Force Insurance Companies to Think Hard About Race
The controversy surrounding the political consulting firm Cambridge Analytica’s use of personal data harvested from social media accounts without the users’ permission is among the first of what likely will be a long series of public debates about how the use of “big data” can shape our lives. And one of the most obvious battlegrounds where we should expect such fights to play out soon is in the insurance industry.
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?