Big Data Analytics in Life Insurance and Disability Insurance


Big Data is rapidly evolving and affecting the life insurance landscape. Big Data Analytics has led to significant innovation and new product development in life science and disability insurance market place. The success of Big Data innovation lies in having the courage and judgement to trust the insight gained, and the strategic will to implement it.

Data providers have now become very critical to many successful use cases. Life insurers can now ask for external data sources to create a complete risk profile and understand their customer before underwriting life or disability insurance. The analytical risk models and scoring can be expanded the footprint to include

  1. Life Style data – This includes data about your house, house members, and neighborhood, profession, employer, recreation activities including sports, cars driven, previous claims, etc.
  2. Healthcare data – This data is available with healthcare providers and laboratories. This data is very crucial to determine the mortality and morbidity risks.
  3. Retail/Grocery/SuperMarket data – This can be very new data set that can be injected into your risk scoring. Retailers store massive amount of data that can identify your family and individual health habbits and life style. The challenge is getting access to these data sets
  4. Banking/Credit card/Retirement savings data – This data set can provide insight into individual’s financial stress, saving pattern and individual’s risk score.

As per BCG Survey in June 2014, Insurers are using big data and advanced analytics to improve business processes and expand into new markets, thereby generating significant revenues and profits. Others are building long-term, trusted relationships with customers. These insurers are offering valuable new or improved products and services in exchange for personal data that customers provide voluntarily. The new offerings may help clients improve their health or may provide access to insurance for people who are considered risky or expensive to serve. The landscape is changing. Companies that get ahead of these shifts will discover new opportunities for efficiency, growth, and innovation.

Companies are also using data to understand distribution. Understanding the quality of business written by different company agents, tied channels or independent brokers is useful. Models are emerging that identify and score the better agents, and this information is then used by the insurer to manage agents and provide incentives for agents to improve behavior. Agents can be scored on various factors, including:

■ Early lapse experience and/or policies not taken up

■ Comparisons of disclosure rates identifying agents or brokers that are good at encouraging policyholder disclosure

■ Sales figures, such as volumes, policy size, etc.

These models also make it possible for the insurer to manage distribution and identify individual agents, brokers, or even broker agency or networks that need improvement.

Opportunities to use internal data are also available to insurers operating in the group environment. Information may be available from sources within the insurer on the employer and/or the employees of a particular group. This could allow the insurer to more accurately understand the risks. An example may be where the insurer has absence management program data, which could be linked to project disability and mortality rates. Such sources of data tend to be more useful where groups are too small to be rated fully on their own experience. It may, however, still be useful for conditions that are changing from year to year for a larger group

There are risks associated with Big Data, although this article did not cover them. Data privacy continues to be an important issue, and recent revelations of government surveillance have further heightened awareness of it.

Many insurance companies are tapping into large, fast-moving, complex streams of big data and applying advanced analytical techniques to transform the way they do business. All life insurers are now converts to the idea of big data in principle. But using big data seems to be low on the executive agenda. In practice, the life insurance industry as a whole has been slow in making use of the data they have access to. The truly transformative potential of these strategies will take time to play out. Insurers that excel at these and other strategies to intensify the customer relationship and move into new markets will catch up to—or even surpass—more nimble companies that are already in the game. Over the long term, these approaches will open up important avenues for growth and innovation for companies willing to experiment now. They will win the best clients, reduce risk, increase loyalty, and create more opportunities to cross-sell products and services.The rest will risk falling behind or ceding the most attractive customer relationships and emerging markets to others.

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