big data

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?

Haven Life is leveraging MassMutual's historical data to give instant life insurance approvals. Using AI and machine learning to derive new value from old data could become an enterprise staple.

Slides from presentations delivered at the 2017 Southeastern Home Office Underwriters Association have been posted at the SEHOUA site. Presentations include:

• Analytics – Mind versus Matter
• Pharmacogenetics: Relevant or Not?
• Give Credit Where Credit is Due – The Value of Credit in Life Underwriting
• The Underwriting Profession at Risk: 4 Steps to Manage Career Morbidity
• Underwriting the High Mileage Applicant
• The Meaning of Underwriting
• Life after Bariatric Surgery
• Mental Illness Update

Jennifer Golbeck, Director of the Human-Computer Interaction Lab at the University of Maryland speaks with LIMRA’s Eric Sondergeld about “Big Social Data” and how to balance the need by industry for insight with the desire for privacy by consumers.

RGA's Raimondo Guerra demystifies data and discusses the future of insurance. Questions or comments?

The Canadian life insurance industry is seeing a wave of innovation across multiple fronts. Evolving customer expectations, shifts in distribution, emerging technologies, big data, and the threat of nontraditional entrants have insurers looking to drastically change the way they do business.

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.

It’s widely understood that a faster and easier life underwriting process is needed in order to penetrate the underserved middle and growing millennial markets. Carriers also believe that a better buying experience would produce more sales – and fewer dropped sales – in the upper middle and affluent markets.

Some life insurers now use data from fitness trackers to lower premiums. But does a policyholder’s number of steps really improve his or her mortality? Despite the link between a sedentary life and the risk of heart disease or cancer being well known, there is no consensus on how many daily steps reduce this risk.

How big is “big data” for life and health insurers? What are insurers worldwide focused on when it comes to predictive analytics? How are they adopting it and what are their expectations for the future? We set out to find answers to these questions through our global predictive analytics survey just released to participants this month.