Mining for a valuable commodity

01 November 2016 Gareth Friedlander, Discovery Life

Big data is a term that encapsulates the large, complex data sets that business use to identify relationships, patterns and trends between data points.

While Big Data is centred on the quantity of data, to a more important degree, the focus should be on how businesses can utilise this data to better understand their clients and so improve their products and services.

Ignoring the asset

Existing Big Data analytic tools are still in their relative infancy, meaning that few businesses are, as of yet, using this asset to its full capacity. But, there is no doubt that big data, when used effectively, can give life insurers, as one example, a competitive edge in the market.

Life assurance companies have masses of standard information on individuals such as policy level demographics and benefit data. Only recently has other useful data, such as information on health and wellness behaviour, achieved the experience level required to make material product design changes.

Given these developments, the use of Big Data analytics in life insurance and other protection solutions is expected to increase. As technology becomes more powerful and integrates with artificial intelligence and machine learning – intelligence that gives systems the ability to learn without programming – the relationships between data points can be identified much quicker. This will enable organisations to offer clients more personalised products and services that adapt to the needs of their client in real-time.

Accurate pricing

Leading life and short term insurers have, since 2002, started combining the benefits of Big Data analytics into the structure of their product offerings.

Underwriting was the first significant area affected as Big Data recognises that lifestyle related client behaviour, such as smoking, inactivity and health measures that affect risk, can change over time. When clients positively change their behaviour their insurance risk is lowered. This reduction in risk should be accompanied with a commensurate drop in a client’s premium or an increase in their relative benefits.

Traditionally, insurers would simply underwrite at point of sale and base their premiums on an average client for that age and risk profile. Big Data has thus allowed a shift from a static underwriting approach to a dynamic underwriting approach that adjusts premiums in line with the changes in a client’s risk profile over the duration of their policy.

Active management

• Improvements in available technology have allowed the collection of new, previously unavailable, health metrics at the start of a policy. This, together with improvements in Big Data capabilities, has allowed some insurers to price more efficiently, reduce the extent of cross-subsidies inherent in traditional pricing models and maximise value for both the healthy and the sick by introducing personal pathways for healthy clients and at-risk clients respectively;
• healthier clients receive upfront premium discounts and other financial rewards over the duration of their policy to incentivise them to maintain their health; and
• other clients, who are traditionally seen as a higher risk, are encouraged to improve their health through managed care programmes that help manage chronic conditions. Through improved management of their condition, clients can unlock more cover and reduce their health loadings over time.

Organisations already have enormous amounts of relevant data and, going forward, they expect quality, real-time data to make a significant difference to the client experience. In the not too distant future, valuable data will come from wearable devices, cars, and even appliances.

Combining all these data sources will allow for improved business process, products and pricing. For example, Big Data may someday allow the often time-consuming underwriting process to be done away with completely, as Big Data makes it possible to simply look at available information to make a quick, yet science-based, decision about client risk and cover.

With advances in artificial intelligence, it may also be possible for future generations to get a personalised life insurance product with premiums and benefits that adjust to their experience in real-time.

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