Life Insurers Increasing The Use of Big Data and Predictive Analytics

life insurance predictive analysis

Big data has become a topic of great interest in the insurance industry over the past few years. Not just ordinary; the main focus has turned to big data. Responses from insurance companies indicate that harnessing the power of big data has become an important factor in the predictive analytics framework. This information can be a huge benefit regarding decisions about pricing, underwriting and the company’s overall business strategy.

Most industries have already caught the Big Data bug and life insurance is no exception. The key is to extract useful insights, which requires the careful planning and execution of advanced analytical techniques and technologies. However, insurance companies need to have the right people, proper systems and the best processes in place in order to be successful.

Karen Cutler, Chief Underwriter at Manulife and a real proponent of how this technology is changing the industry says, “Through predictive analytics we understand our business better than ever. We expect to use analytics and modelling with our living benefits products in the future, but right now we are focusing on life insurance.”

P&C and Advanced Analytics

Property and casualty (P&C) insurance was one of the first in the industry to use advanced analytics for improved risk selection and to offer their clients new products. Auto insurance providers, for example, offer coverage on a usage base. Their technology monitors driving behaviour and rewards careful drivers with a discount on premiums. Tech-savvy Millennials find this idea particularly appealing, especially if they haven’t yet established a good credit score or long good driving history.

With so much potential in this area, it will be very beneficial for life insurance providers to learn from the knowledge the P&C industry has acquired over the years. Of particular importance is how big data and predictive analytics can be best used and deployed. Some P&C insurance companies made huge initial investments on infrastructure and applications, before considering market deployment. Life insurance companies should first think about how these tools will be used, set their goals, chart their course and then invest in the areas they need to succeed.

Data sources already in use or in consideration include:

  • credit scores
  • administrative systems
  • medical records
  • prescriptions
  • claims data
  • social media
  • website clickstreams

Combating insurance fraud is another area the industry is using analytics. Canada’s main insurance providers have amassed huge amounts of data over generations, allowing them to spot questionable applicants. As Cutler explains, “The majority of people do provide full disclosure on their insurance applications. One of the biggest challenges we have found in North America over the years has been the issue of smoking disclosure and tobacco use. We use analytics to identify the people that have a high probability of being a smoker. Because of that, we need to test those people before offering insurance.”

Moving Towards the Future

While many life insurance companies are in the early stages, most expect big data and predictive analytics usage will dramatically increase over the next few years. Many life insurance companies also anticipate the expansion of data applications and new sources for data collection.

According to a Willis Towers Watson survey, only 8% of insurers are actively using the data they collect to assist in making important business decisions. Respondents expect this to change in a big way: 62% say they plan to take full advantage of big data and predictive analytics over the next two years to:

  • expand relationships with customers
  • transform business models
  • improve internal performance management
  • enhance the customer value proposition

The challenge for insurance companies is what data to collect, where to collect it and what to do with the information. Currently, administrative systems (claim data, agents, underwriting data and postal code) is the top data source. Although these will continue to be reliable sources, additional sources such as emails, websites and social media will also be used.

According to Lorne Marr, Founder and Director of New Business Development at LSM Insurance, implementing the use of big data and predictive analytics has a few pros and cons.

Benefit to Consumers

1) Reduce the cost / number of medical tests required. More and more insurance companies are reducing the number of intrusive tests. Numerous studies have shown Millennials are not fans of medical tests – they want everything done quickly and are not fans of huge delays. 
2) Could reduce the cost to insurers which could be passed on to the policyholder. 
3) Life Insurance policies should be issued quicker.

The Drawbacks

1) Many consumers have privacy concerns and do not like the increased use of outside data to assess insurance applications.
2) It may be more challenging to issue policies at preferred rates without the supporting medical tests – preferred rates can save consumers up to 35%.
3) This may lead to a higher number of claims being contested.

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