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TDI China in Focus-Digital Life Insurance Data

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Driving better life insurance product designs and services through data

The insurance industry has been going through accelerated changes in recent years driven by digitalisation. According to Mckinsey & Company, 47% of life insurance customers prefer an omni-channel journey even though it is a traditional product. In addition, it is estimated that digital customer on-boarding could also advance the purchasing cycle by 50-70% and reduce administrative expense by 20-30%. Across the world, many insurers have started digitalising their customer journeys: a life insurer in the US launched for the first time an illustrated calculator which takes customers through an easy assessment to graphically estimate their protection needs; similarly, a life insurer in Singapore has designed a simple online quoting tool, which allows customers to easily preview and compare their expected coverage, terms and premium amount.

Despite the obvious benefits brought by enhanced customer journeys, it is only part of the puzzle for life insurers in terms of digitalising the value chain. We see an acceleration of emerging digitalisation models across the world, with China in particular, where data is more broadly collected and utilised (with customer consent) through smartphones, wearables and various Internet of Things (IoT) sensors, social media and digital distribution partners/channels.

How can insurers make use of this opportunity to help close the protection gap? In order for us to support our clients and the industry to do this, we need to first understand our current and potential customers better and possibly in a different way, and this is how advanced data analytics could help us improve our approach and insights:

Modelling the ideal client

Advanced data analytics helps us build improved prediction models by helping us understand and analyse the factors and drivers to outcomes such as customers with higher propensity to lapse, renew, purchase a second product and what that product may be. These models are based on the individual attributes of a current or potential customer (not a class, type or other characteristic from a group of customers). This helps us take a more targeted and personalised approach to understanding the individual customers who make up inforce portfolios, including their unfulfilled needs and hence opportunities to close the protection gap.

Propensity to buy

With the right data, we can now predict the needs of our potential customers and propensity to purchase coverage. This allows us to better focus on providing tailored customer journeys and products with the right coverage. It also benefits the insurer in the form of reduced distribution costs and increased customer satisfaction.

Risk Triage

Predictive analytics means we can now better engage with potential customers and as a result, streamline and customise the purchase process for their needs and situation through instant quotes, simplified policy issuance or full underwriting.

Behavioural Economics

Behavioural Economics helps us determine the optimal way to approach and engage potential consumers by recognising the way one thinks they will logically act is not always/usually how they will in actual fact behave. By leveraging data about a potential consumer, we can determine trends, preferences and patterns that help us apply the economics of behaviour to improve the customers’ insurance experience and relationship with the insurer.

Across the globe, Swiss Re has been collaborating with our clients and ecosystem partners in devising the best solutions for life insurance leveraging data:

  1. In-force portfolio

Partnering with a digital platform-based diabetes management program, Swiss Re conducted a data-driven study to deduce the potential health improvement impact on one of our insurance client’s in-force portfolios. The diabetes management partner offers an easy-to-deploy customer engagement platform on which the diabetes management program sits.  The program is designed to manage, improve and even reverse, type 2 diabetes, pre-diabetes and other metabolic syndrome. The impact on the in-force portfolio is significant: a 1-5% claims improvement is predicted, with reversal of diabetes for 39% of diabetics, leading to better health outcomes overall. During the study, 70% of the participants were still engaged 12 months later, demonstrating its potential to improve policyholder persistency.

  1. Dynamic pricing

More often than not, customers with Type 2 diabetes find themselves in a difficult position when they want to purchase life insurance as many have pre-existing health issues and illnesses. Swiss Re embarked on a journey with mutual The Exeter, on the Managed Lives product which is a bespoke life product that for people with high BMI or Type 2 diabetes. With the goal being to support customers manage and improve their health, monthly premiums are periodically updated to reflect the policyholder’s health status. With the policyholders’ consent, the health status and blood glucose level are continuously monitored through a diabetes disease management mobile app, and insights from the data shared are used to alert the customer and encourage engagement in health improving behaviours and activities.

  1. Mortality Risk Scoring

LifeScoreLabs and Swiss Re have partnered to bring more transparent underwriting solutions to life insurance and consumers. LifeScore360, developed by MassMutual, is an advanced risk assessment algorithm leveraging millions of data points from over decades of experience to produce a comprehensive mortality score. There is high potential to improve the current automated underwriting engines by embedding LifeScore360, or used on its own, to create best in class end-to-end underwriting, pricing consulting and product deployment to the industry. This solution aims to deliver a new standard in measuring mortality risk and enabling accurate, efficient and transparent underwriting decisions.

What will happen in Asia, particularly in China?

Swiss Re, together with our insurance clients, have launched data-driven solutions in the medical insurance space in Asia, starting with our partnership with Muang Thai Life Assurance on a dynamic pricing diabetes health insurance policy in Thailand. We have been working on leveraging this diabetes health solution in China, as the diabetic population is expected to increase to 150 million by 2040. Additionally, as the population in China ages – more than 248 million of the population are expected to be over the age of 60 by 2020 – we see it as another area which we could make use of data analytics to close the protection gap. Indeed, we have been working closely with clients on retention management case studies and more. If you would like to hear more about it, please don’t hesitate to get in touch with us.


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