Tag Archives: jet-underwriting

2 Ways to Innovate in Life Insurance

Individual life insurance ownership in U.S. has been decreasing over the past decade, and the figures are even more depressing when we look at the figures over the past 50 years. Life insurance ownership (both group and individual) among U.S. adults has dropped from 70% of individuals in 1960 to 59% in 2010. The number of individual policies owned by U.S. adults has dropped from 59% in 1960 to 36% in 2010, according to the Life Insurance and Market Research Association (LIMRA). The world has seen accelerated change over the past several decades, and, as entire industries transform, even leading and innovative companies can get trampled. The life insurance industry is no exception. The figures clearly demonstrate the slowing demand for life insurance. Are we seeing the “death” of life insurance, or is this just a temporary “blip” as the industry re-designs itself for changing demographics? Are there innovative business models that can change the situation?

The Case for Big Data and Analytics

The life insurance industry needs to innovate and needs to innovate fast. Innovation has to come from understanding end consumer needs better, reducing distribution costs in addressing these needs and developing products that are less complex to purchase. By leveraging new technologies, particularly new sources of data and new analytics techniques, insurers will be able to foresee some of these changes and prepare for disruptive change.

There are at least two distinct ways in which new sources of data and analytics can help in the life insurance sector.

  • Underwriting: Identifying prospects who can be sold life insurance without medical underwriting (preferably instantaneously) and speeding up the process for those who do require medical underwriting
  • New non-standard classes: Identifying and pricing prospects who have certain types of pre-existing conditions, e.g., cancer, HIV and diabetes.

Predictive Modeling in Underwriting

A predictive model essentially predicts a dependent variable from a number of independent variables using historically available data and the correlations between the independent variables and the dependent variable. This type of modeling is not new to life insurance underwriters as they have always predicted mortality risk for an individual, based on variables of historical data, such as age, gender or blood pressure.

With the availability of additional data about consumers, including pharmacy or prescription data, credit data, motor vehicle records (MVR), credit card purchase data and fitness monitoring device data, life insurers have potentially a lot of data that can be used in the new business process. Because of privacy and confidentiality considerations, most insurers are cautious in using personally identifiable data. However, there are a number of personally non-identifiable data (e.g., healthy living index computed by zip code) or household level balance sheet data that can be used to accelerate or “jet-underwrite” certain classes of life insurance.

Some insurance companies are already using new sources of sensor data and applying analytics to personalize the underwriting process and are reaping huge benefits. For example, an insurer in South Africa is using analytics to underwrite policies based on vitality age, which takes into account exercise, dietary and lifestyle behaviors, instead of calendar age. The insurer combines traditional health check-ups with diet and fitness checks, and exercise tracking devices to provide incentives for healthy behavior. Life insurance premiums change on a yearly basis. The company has successfully managed to change the value proposition of life insurance from death and living benefits to “well-being benefits,” attracting a relatively healthier and younger demographic. This new approach has helped this company progressively build significant market share over the past decade and exceed growth expectations in the last fiscal year, increasing profits by 18% and showing new-business increases of 13%.

Pricing Non-Standard Risk Classes

In the past, life insurers have excluded life insurance cover for certain types of conditions, like AIDS, cancer and stroke. With the advances in medical care and sensors that monitor vital signs of people with these conditions on a 24×7 basis, there is an opportunity to price non-standard risk classes. Websites that capture a variety of statistics on patients with specific ailments are emerging. Medical insurers and big pharmaceutical companies are leveraging this information to understand disease progress, drug interaction, drug delivery, patient drug compliance and a number of other factors to understand morbidity and mortality risks. Life insurers can tap into these new sources of data to underwrite life insurance for narrower or specialized pool of people.

For example, a life insurance company in South Africa is using this approach to underwrite life insurance for HIV or AIDS patients. They use extensive data and research on their HIV patients to determine mortality and morbidity risks, combine their offering with other managed care programs to offer non-standard HIV life insurance policies. They have been operating over the past four years and are branching out into new classes of risk including cancer, stroke and diabetes.

Surviving and Thriving in the World of Big Data

The examples we have provided are just scratching the surface of what is likely to come in the future. Insurers that want to leverage such opportunities should change their mindset and address the challenges facing the life insurance sector. Specifically, they should take the following actions:

  • Start from key business decisions or questions
  • Identify new sources of data that can better inform the decision-making process
  • Use new analytic techniques to generate insights
  • Demonstrate value through pilots before scaling
  • Fail forward — institute a culture of test-and-learn
  • Overcome gut instinct to become a truly data-driven culture

In summary, life insurance needs to innovate to be a relevant product category to the younger and healthier generation. Using new sources of big data and new analytic techniques, life insurers can innovate with both products and processes to bring down the cost of acquisition and also open up new growth opportunities.

What cycle-time improvements have you been able to achieve in the life new-business process? How well are you exploiting new data and analytic techniques to innovate in the life insurance space?