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Article | Insurer Insights

The coming of age of the analytical life insurer

Insurance Consulting and Technology

By Matthew Edwards | March 12, 2020

Alastair Black and Matthew Edwards walk us through the kind of advanced analytics ‘factory’ that could help more life insurers to benefit from more sophisticated underwriting and pricing.

Sophisticated analytics, underwriting and pricing - perhaps incorporating elements of machine learning and artificial intelligence (AI) - have increasingly become key competitive factors in many property and casualty (P&C) markets around the world. While still relatively early days in life and health by comparison, some innovative companies are seizing the opportunities to harness some of the same benefits.

Analytics nurseries

Protection products are a natural starting point, because the markets tend to be larger and are typically served by multiple competitors offering fairly homogenous, slimmer margin whole of life, income protection and critical illness coverages. In the UK, where the market is also influenced by a small but growing proportion of direct sales, the response has been quite apparent. Around 75% of the UK protection market, for example, is already served by companies that are using some or all of Willis Towers Watson’s award-winning Radar analytics and pricing suite.

Around 75% of the UK protection market, for example, is already served by companies that are using some or all of Willis Towers Watson’s award-winning Radar analytics and pricing suite.

Broadly similar market characteristics – simple products in a competitive market – also mean that annuities are another product where life insurers have been looking more at the competitive advantage and potential payback on analytics investment. Looking outside underwriting and pricing moreover, there are also opportunities for life and health insurers to mirror how more P&C companies are using analytics and automation to better understand trends in experience and customer behaviour.

In the core areas of pricing and underwriting, the benefits that companies are looking to reap from such investment include enhanced pricing flexibility, better responsiveness to market developments, material operational efficiencies and reducing the risk of costly pricing errors. In conjunction with those, there’s the not insignificant fact that prices that could previously take up to six months to update, often with extensive back and forth with the IT department, can now be live in the market within hours, complete with full review and governance. Even changing rating structures or adding new rating factors can be done quickly and easily.

Which brings us to what’s involved.

The diagram shows the main stages (the factory floor) of a fully automated and controlled underwriting journey in Radar. Section 1 is policyholder information, with an arrow for data query pointing to section 2 Risk Rates. You then have two options, 1 arrow pointing back to Policy holder information with request more information, or you then move on to section 3 premium calculation. Follow the arrow governance to take you to section 4 Market, then section 5 management information and finally section 6 rate monitoring and adjustment. You then have the option to go back to request more information which takes you right back to the start.
Figure 1. The main stages of a fully automated and controlled underwriting journey in Radar

Policyholder information

This is where a customer requests a quote and is asked to provide information relevant to the policy. The policy system can pull in information from a wide range of sources, so quote information can be enriched with data from other sources such as Fitbit outputs, zip code/postcode information, distributor information and, where permitted, personal information from other data sources. The system can also be set up to incorporate automated decision points about whether more information is needed for cases such as where an applicant has a specific named condition or where the sum assured is above a particular threshold.

Risk rates

With the customer data gathered and prepared, attention moves to building the underlying view of the risk of that customer. In other words, to put it bluntly, how likely they are to die or claim.

This could happen in several ways, including calling the existing rate structure, but could also potentially use generalised linear models (GLMs) that incorporate claims experience, models that incorporate medical judgement (such as Willis Towers Watson’s PulseModel), reinsurer rates, machine learning and AI-generated information, or a mix of some or all of them.

As an example of the benefits that can come from these approaches, some companies with which we’ve worked that have introduced GLMs have found the increased segmentation capabilities provide risk differentiation of around +/- 50% (identifying that high mortality groups show double the mortality of low mortality groups).

Premium calculation

After the risk assessment comes deciding what you are going to charge. Even with a simple expenses-plus type approach, a loading for risk based on a more in-depth assessment is likely to have advantages. Similarly, rate structures that are based on supply/demand or attaining a certain competitive position in the market can be applied with less risk of adverse selection using customer demand behaviour data. Such data also opens the way to price optimisation in markets where it’s possible.

Perhaps the key thing though is the flexibility, efficiencies and ease of governance the system provides. Although unlikely to be required in protection or other life markets immediately, it can support updating prices daily, providing a competitive advantage. It allows insurers to change rating structures easily, and to ease the transition from a fixed rate table to more advanced formula or algorithm driven pricing.

Market distribution

All of the technical wizardry in the world is worthless if you can’t get your rates to the market in a timely manner. Using industry standard API technology, the system can be called by both internal and external systems, providing channel-agnostic digital distribution. It also maintains complete transparency and flexibility over variable changes to pricing for the likes of advisors, affinity partners such as banks, and price comparison sites. Specific benefits for different distribution options could include making real-time information available to call centre staff about factors such as likely customer value and upsell potential or combining company and agent data to produce channel-specific analysis.

Feedback loops

The system allows information on new business, underwriting decisions, lapses and claims to be fed in to create bespoke management reporting templates. These can be tailored to specific business metrics such as agent performance and market segmentation, and used to inform pricing and business decisions.

The feedback loop also replicates traditional life actual versus expected methods, allowing the import of standard tables. In combination with credibility results, the analyses can then feed into the revision of rates used for new business in the risk rates component.

Moving out of the analytical sandpit

For now, these kinds of analytics ‘factories’ are the exception rather than the rule in life insurance and, where they do exist, they are typically less comprehensive in scope and scale.

Yet, as the UK protection market shows, market drivers and the competitive environment can move quickly once disrupted. The emergence of the analytical life insurer in a wider array of markets may not be far off.

In that eventuality, those that opt to soldier on with often clunky information technology may not only have to suffer the enduring frustration of drawn out, costly and heavily manual processes, but risk being outmanoeuvred on product design, price and customer experience.

Contact - Asia Pacific

Lydia Williamson
Senior Consultant
Insurance Consulting and Technology

Contact - EMEA

Insurance Consulting and Technology

Matthew Edwards is an actuary based in London, where he leads the demographic risk and analytics team at Willis Towers Watson. He previously worked for Aviva, where he was Life Director in the Milan office for several years. He leads the UK actuarial profession’s mortality tables and projections group (CMI) and co-leads the new COVID-19 actuaries’ response group.

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