CPD: Adviser briefing – the future of client-centric life underwriting


There is a transformation taking place in life insurance underwriting, and the potential implications for the customer experience and advice process are big.


Underwriting is a foundational element of life insurance. By ensuring lives are admitted to the life insurance pool at the price and terms that accurately reflects their risk, life insurance can remain sustainable, ensuring it is there when most needed – claim time.

But underwriting has long been a friction point for clients and advisers alike, perceived as a complex, lengthy process designed to shut people out rather than let them in. In an era when consumers expect instant, real-time decisions and a streamlined digital environment, underwriting is seen to represent the very opposite of a contemporary experience – characterised as involving long paper-based forms, stringent medical requirements, and complex, judgemental questions. High drop-out rates at application stage – estimated by some experts to be around 80%[1] – are a clear indicator of consumer sentiment on this topic.

But the good news is that life insurers have been listening, for quite some time in fact. And slowly but surely, the underwriting process has become more client-centric, with simpler questions, higher non-medical limits, and faster decision-making times helping reduce the friction and reduce the barriers to obtaining coverage.

The even better news is that this innovation is constant, and the future of life insurance underwriting is likely to be even more client-centric, as insurers leverage the power of AI, the big data and wellbeing revolutions, and behavioural science, to reshape the life underwriting process into one that offers positive, meaningful outcomes to clients beyond the simple issuance of a policy.

In this article, we will examine the megatrends being harnessed to transform not just underwriting processes, but the entire 21st century life insurance client experience.

The wellbeing trend

The wellness industry is experiencing remarkable growth, as consumer interest in health and wellbeing continues to surge. This trend is evident through the popularity of fitness classes, wearable technology, and mindfulness apps, with wellness becoming an integral part of daily life for many.

The size of the wellbeing economy is staggering, with data released by the Global Wellness Institute (GWI) estimating annual global spend on health and wellbeing to be around $4.4 trillion[2]. -Australia ranks as the 10th largest wellbeing economy, with spending of $84 billion.  Accounting for 5.1 per cent of global GDP, roughly one in every $20 spent by consumers worldwide goes to wellness products and services according to GWI.

Experts generally categorise wellness into six dimensions: health, fitness, nutrition, appearance, mindfulness, and sleep.

Advancements in sleep science have reinforced the view that quality sleep is the foundation of good health and amplifies the benefits of regular exercise and a healthy diet. As a result, sleep is rapidly emerging as a significant wellness focus, with consumer demand for sleep-related products and solutions continuing to rise.

How sleep could shake up life underwriting

One outcome of this growing focus on sleep is the use of digital devices such as the Apple Watch, and wearables (like the Fitbit), to monitor sleep patterns. This in turn is making sleep data more widely available, representing a resource with the potential to reshape underwriting.

In 2022, South African Actuaries Nicole Kriek and Matan Abraham delivered the findings of their research into the impact and implications of sleep patterns as a new rating factor for life insurance[3].

Based on a series of literature reviews and 10 months of anonymised sleep tracking data, Kriek and Abraham showed evidence of a clear link between lack of sleep and poor health outcomes across all body systems. More importantly, they proved a clear correlation between lack of sleep and mortality.

Their research then explored how sleep data could drive improved life insurance underwriting, the objectives being to:

  • determine how sleep data can be used to identify undiagnosed conditions not assessable by initial underwriting
  • determine how sleep data can be used to provide early warning signs of the future onset of disease
  • provide an indication of the severity and effective management of disclosed conditions
  • identify trends in sleep data over time that can be used to trigger interventions to improve lifestyle wellness.

Poor sleep can be caused by many factors, some of which may not be immediately visible, or even known to the life insured, including undiagnosed conditions, and external factors such as stress.

By introducing sleep data into the underwriting process, life insurers not only have further data from which to assess the health of applicants more accurately, they will be able to identify individuals suffering sleeping issues, and provide resources to help them improve their overall quality of sleep and quality of life.

People expect their life insurer to support their wellness

Consistent with the growing interest in wellness products and services, individuals are embracing those brands that offer support, resources, and incentives that promote healthier living.

This is certainly true with life insurance, with research by reinsurer SCOR finding nearly one third of consumers consider health and wellness guidance to be part of the life insurance proposition[4].

While many insurers have responded to this demand, by developing integrated wellness offerings, there is still scope for improvement, with the same SCOR study finding that the amount of health and wellness guidance (and indeed financial tips) offered by life insurers was falling short of expectations.

Although underwriting is at the very beginning of the life insurance customer journey, the nature of the process is such that it can deliver positive health outcomes to applicants, in a number of ways:

  • the nature of the questions can alert the applicant to potential health issues, by signposting symptoms (e.g., night sweats, unexplained weight loss) of potentially serious, but undiagnosed, conditions
  • the use of loadings and exclusions provided a similar signpost, matched with a financial incentive to modify risk factors
  • similarly, some insurers, as part of their underwriting process, provide access to resources to help manage conditions such as obesity and high blood pressure
  • in the event that medicals are required as part of the underwriting process, this may also uncover any previously unknown issues
  • the opportunity to have loadings reviewed can be used to create a framework for behavioural change, with specific targets and timelines.

Leaning on behavioural economics

Along with the relative complexity (and length) of the underwriting process, the accuracy with which individuals provide health and lifestyle information can also be problematic, with incomplete, inaccurate, or false data undermining the assessment of risk, and potentially leading to adverse selection, and disclosure related disputes at claim time.

In this sense, an underwriting process that is simple, faster, AND more accurate would seem like nirvana. With the help of behavioural science, this nirvana may well be achievable.

Real life is not like the textbooks, and human decision making is not rational, it is largely irrational, driven by a complex array of emotions, biases, and mental short cuts. Behavioural economics is a science that seeks to understand the how and the why of decision making.

Historically, insurance application processes have been grounded in rational economic theory, with under-disclosure and non-disclosure viewed by some as being a rational attempt to mislead, for the purposes of financial gain. Over time however, the understanding of behavioural economics has helped insurers understand that other factors are at play, and by understanding these factors and adjusting question design accordingly, the accuracy and simplicity of the health disclosure process can be dramatically improved.

In an underwriting context, there are generally four main drivers behind inaccurate disclosure:

  • framing
  • honesty beliefs
  • cognitive resources, and
  • negative behaviours.


Framing is a cognitive bias which can drive people to make drastically different decisions, even when presented with the exact same data, based on whether the options are presented with negative or positive connotations.

The classic demonstration of this was an experiment[6] by Kahneman and Tversky, who presented the same scenario in two different ways. The scenario related to a new disease that had emerged in a country and was expected to claim 600 lives. Participants had to choose between option A and option B. These options were the same in each scenario, one was just expressed differently.

Honesty belief

A powerful concept in behavioural economics is that of the dishonesty threshold, that is, people will only be dishonest to the point they can still feel good about themselves. In an insurance context, intentional inaccuracies are often driven by psychological motives, for example they are aware of a health condition, but they make their own decision as to whether that is relevant, perhaps because it happened a long time ago, or perhaps because their doctor told them it was all ‘under control’. Similarly, somethings are difficult to admit, and people can be in denial about conditions such as obesity, excessive drinking, or some chronic health condition.

Cognitive resources

The human brain lacks the cognitive resources to consciously make the thousands of decisions required of each day, which is why we develop mental short cuts. In his book, ‘Thinking, Fast and Slow,’[7] Daniel Kahneman described two systems of thinking:

  • System 1 – Fast Thinking, based on intuition and automatic processing.
  • System 2 – Slow Thinking, which is deliberate and requires more work.

In an underwriting context, understand the way people process information, and use short cuts, can help insurers explore ways to ‘nudge’ them towards desirable behaviours.

Negative behaviours

Some people attach shame or stigma to certain behaviours, and may be inaccurate in their responses as a result.

Smoking is a classic example, alcohol consumption another.

How behavioural economics can help

Behavioural economics can increase application accuracy in a number of ways, including:

  • making it easier to be accurate
  • making it easier to be truthful.

Accuracy is often undermined by the complexity of the question being asked, and reducing the cognitive load required to answer a question may help improve the accuracy of the answer. This can be done in a number of ways, including:

  • using simple, everyday language – avoid ambiguity
  • using numerous simple questions rather than one long multi-faceted question
  • prompting memory by listing possible answers – use drop-down menus, scales, and other methods to replace the usual binary ‘Yes’, ‘No’ response
  • asking about experiences (if possible), rather than illnesses.

Questions about lifestyle choices (such as tobacco usage and alcohol consumption) can be framed in a way that destigmatises them, for example by focusing on how much they consume, rather than a binary, judgemental ‘have you ever smoked, do you ever drink?’.

Insurers and reinsurers continue to evolve underwriting approaches based on these behavioural economics principles, and keen observers will have noted that over the last few years, the structure and nature of questions asked at application stage has changed, sometimes in a major way, sometimes in a more subtle way. This evolution will undoubtedly continue.

Big data, digital technologies, and Artificial Intelligence

Arguably, it will be technology, powered by big data and artificial intelligence, that has the most scope to drive customer centric innovation in underwriting, underpinning faster, more personalised decisioning, and a more contemporary, omnichannel client experience.

Life insurance has long been a data driven industry. Mortality tables, after all, are based on life expectancy data going back over a century. When that data is supplemented with the wealth of data from other sources, including an insurer’s own claims experience, lifestyle data, global mortality, and morbidity data (from reinsurers), an even more accurate picture can be built up of the risks associated with various health, lifestyle, and psychological factors. In simple terms, the more data an insurer has, the more precise their decisioning can be.

Internationally, some life insurers already incorporate non-traditional risk rating factors – such as education, driving history, even social media activity, into their underwriting. In an ideal world, consumer sovereignty over their own data (via the Consumer Data Right) will give individuals the ability to grant access to their personal data, such as their electronic health records and health insurance claims history, opening up the potential for faster, more accurate underwriting.

(Today, speed is everything. Indeed, Deloitte’s research[8] on Life Insurance underwriting suggests that the likelihood of prospects buying a policy once they apply increases by around 20% as the underwriting and application process gets closer to real time).

When that big data is coupled with developments in artificial intelligence, the possibilities become even more exciting.

Artificial intelligence, including machine learning, has the capability to analyse vast volumes of unstructured data, recognising patterns and producing insights that would otherwise take months. Aside from the incredible speed and granularity of underwriting that AI can drive, the insights uncovered could be turned into new risk factors (e.g., gym membership) that are incorporated into the underwriting process, allowing greater personalisation, and potentially opening up client access to more cover.

Diagnosing diabetes with AI and 10 seconds of voice recording

The intersection point for all these developments may well be our digital devices, including our phones and wearables.

Just using the Apple ecosystem as an example, between their phone and Apple Watch (the highest selling watch in the world), users accumulate a vast array of data, including steps, heart rate, sleep patterns and so on, that can be used to assess health. The cameras and microphone within those devices are also powerful sources of data, especially when combined with the latest developments in disease diagnosis.

Using the camera to send photos of skin cancers is one example. More recently, scientists have found a way to scan for type 2 diabetes using AI and just 10 seconds of voice recording!

According to Medical News Today[9], researchers in India have developed a highly accurate model tool by analysing six-to-ten-second voice clips from 267 study participants — some of whom had diabetes and some of whom did not — recorded on their smartphones.

The study found that changes in pitch and voice strength were significant for diagnosing type 2 diabetes.

In summary

Historically, seen as a complex and lengthy process, creating barriers for potential clients, underwriting in the life insurance industry is experiencing a profound transformation, evolving in line with several megatrends, and making strides towards a more client-centric approach by harnessing the power of big data, AI, and behavioural science,

One significant trend shaping the future of underwriting is society’s growing focus on well-=being. The wellness industry’s rapid growth, with global spending estimated at $4.4 trillion, is reshaping the way insurers assess risk. Sleep, in particular, is gaining prominence as a focus of wellness, with the potential to reshape underwriting through the power of sleep data collected through wearable devices like the Apple Watch.

Consumers expect insurers to support their wellbeing, and this expectation is driving the development of integrated wellness offerings. By engaging applicants with thoughtful questions and tailored resources, underwriting can become a tool for positive health outcomes.

Behavioural economics plays a pivotal role in improving underwriting accuracy by understanding the psychological factors that influence disclosure. By framing questions positively, addressing honesty beliefs, and considering cognitive resources and negative behaviours, insurers can enhance the precision of health disclosure.

This ongoing transformation promises a more streamlined, accurate, and personalised underwriting process, ultimately benefiting both insurers and policyholders.


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[1] https://www.linkedin.com/pulse/dont-think-underwriting-customer-experience-rajesh-singh/
[2] https://retailbeauty.com.au/australia-ranked-as-10th-largest-wellness-market-in-the-world/
[3] https://www.actuaries.digital/2022/05/26/how-the-foundation-of-health-can-shake-up-insurance-underwriting/
[4] https://www.scor.com/en/expert-views/consumer-view-life-insurance-market
[5] https://www.scor.com/en/expert-views/consumer-view-life-insurance-market
[6] https://www.scor.com/en/expert-views/simplified-underwriting-behavioural-lens
[7] https://thedecisionlab.com/reference-guide/philosophy/system-1-and-system-2-thinking
[8] https://www2.deloitte.com/content/dam/Deloitte/ke/Documents/financial-services/Insurance%20Outlook%20report%20EA%20-%20Interactive.pdf
[9] https://www.medicalnewstoday.com/articles/ai-10-second-voice-clip-help-diabetes-diagnosis

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