Life Insurance 2.0 & the Death of the Health Exam

Life Insurance 2.0 & the Death of the Health Exam

Can life insurance companies adapt predictive analytics into their business model? This was the question posed during a live presentation at InsureTech Connect 2016.

Discussing the opportunities and barriers to implementing predictive analytics in life insurance are highly knowledgeable industry executives Jamie Hale of Ladder, Elliot Wallace of Lexis Nexis, Sean Conrad of Hannover Re, Troy Thompson of Legal & General America, and Jeremy Hallet of Quotacy.

A Traditional Approach to Life Insurance

Life insurance plays a key role in protecting families and individuals from the financial impact of uncertain mortality. Life actuaries have created fairly accurate estimates of life expectancy to develop mortality tables. These tables estimate the mortality of the insured population, while underwriting methods help in assessing the risks of the individual. Although these conventional methods have worked well for many years, the costs associated with the process have stayed high, which translates to more expensive insurance premiums for consumers.

The current process in obtaining affordable life insurance includes a medical exam, blood draw, and a urine sample. This tends to be time-consuming and expensive, so life insurers are exploring methods to streamline the process while reducing expenses in order to make life insurance more attractive to consumers.

Applying Predictive Analytics to Life Insurance

Predictive analytics is the processing of large data sets to identify or make inferences about meaningful relationships, and the using those inferences to predict future events.

By using predictive modeling, experts believe that underwriting life insurance can be accurately performed using non-medical information, such as data on lifestyle and behavior. Insurance companies are analyzing these modeling tools both for lowering costs by making better predictions of course, but they also hope to address the low penetration among millennials who eschew life insurance.

As Wallace notes, the technology already exists to underwrite policies based on nothing more than name, address and date of birth to assess risk classification. But one significant barrier to further implementation remains: the Fair Credit Reporting Act (FRCA). The FRCA controls many of the rules and regulations surrounding life insurance. Consumers must be able to challenge the decisions made by life insurance companies, which is why the FRCA is so critical.

While life insurance companies have begun to use data analytics and statistics, they have not integrated predictive analytics to streamline the purchasing and underwriting process. Other lines of insurance have adopted methods such as credibility techniques, generalized linear modeling and credit scoring modeling.

The Death of the Health Exam?

If life insurers can find ways to implement predictive analytics, it may mean that medical exams will no longer be a requirement to purchase a life insurance policy. The categories of data that might be used in the future could be such qualitative things as professional licenses, education, residence ownership, and even social media.

Although it is time-consuming and costly, underwriting plays a critical part in the life insurance process. To generate additional value and break the barriers that prevent millennials from participating in life insurance, predictive analytics may become the method of the future to streamline the process.

Laws like the Fair Credit Reporting Act still prevent the adoption of predictive modeling in final decision-making, although underwriters can use data to suggest areas that need more investigation. Ultimately, it seems fairly likely that predictive modeling will be used to make final decisions and this will eliminate the need for the medical exam entirely.


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