Digitalisation and the spread of the internet and mobile technology have impacted a number of industries in recent years, often transforming them beyond recognition. There is little doubt that technology will have an enormous impact also on the insurance industry over the next decade, across the value chain from product development and underwriting through to distribution, services and claims. In the near term, the largest impact is likely to be on underwriting and distribution.
Lengthy, convoluted and invasive underwriting processes have long been viewed as an impediment to reaching and engaging with the impaired or uninsured consumer segments, and is a pain for the applicants. There has been a constant and fierce demand to make insurance easy-to-buy, enhancing the customer experience associated with buying insurance.
Traditional underwriting techniques to select risks are effective, but the process is time consuming and involves high costs. New data sources, platforms to store and analyse data, and new data mining technologies could potentially streamline the underwriting process, reduce invasiveness of risk management and in a holistic way, improve risk selection and policy pricing.
New alternative data sources can be used to assess risk
The traditional underwriting in life insurance requires evidence from customers on their own (and sometimes their family’s) medical history, alongside third-party information. New data sources – from Electronic Health Record (EHR), connected devices to social media – provide alternative data sources that insurers could use to assess and price risk.
Getting the medical records of an applicant to underwrite a policy is currently a cumbersome and time-consuming process, with most of the information provided in print format. Ability to instantaneously assess medical history with the individual’s consent in digital form eliminates the need for underwriters to search and wait for the information, effectively streamlining and speeding up the underwriting process, and significantly reducing costs.
Data from health monitoring devices such as Apple’s HealthKit and Jawbone’s UP on physical activity levels, diet, sleep patterns, heart rate etc may also become useful. It is currently challenging to interpret these data for underwriting purposes, because there is not enough research and experience to link the indicators to health outcomes with a comfortable degree of reliability.
Personalised pricing in real time
Some companies are launching products that make use of these data, though at the moment they have a low level of credibility on the underwriting side and are more of a customer engagement/retention tool; but in time this should improve.
An increasing ability to measure and monitor risks on an ongoing basis could also open opportunities for life insurers to personalise pricing in real time, adapt products over time, and expand insurability for augment pricing for conditions where life and health risks can be mitigated by healthier lifestyles and behaviours.
For example, a healthy diet and increased levels of exercise are known to improve outcomes for people suffering from chronic conditions such as high blood pressure and diabetes. Insureds who alter their behaviours in a positive way may qualify for lower premium rates.
Use of genetic data
Another prospective source of information is the genetic profile of an individual.
Genomic knowledge is evolving rapidly, and genetic data, if available to insurers, could facilitate more accurate prediction of risks by illuminating disease susceptibility, disease-specific genes and pre-disposition to certain conditions.
Genetic testing is already aiding the diagnosis, prevention and treatment of certain diseases. As its benefits become more widespread, health risks for many individuals may be lowered, making them insurable at a better rate.
Cognitive computing will push automated underwriting to new frontiers
Automated underwriting has been a growing trend in life insurance. Developments in cognitive computing – the simulation of human thought processes in a computer model – will advance automated solutions by bringing more consistency to underwriting decisions and by making the process faster and more cost-effective.
Integration of the learning capabilities of cognitive systems, and also their voice recognition and text reading algorithms, will make it possible to extract meaningful information from many sources of data, including unstructured medical reports.
Cognitive systems can be developed to read an applicant’s information, put it in context, extract all relevant facts, compare with the rules and guidelines in underwriting manuals, make a decision on the application, and set a premium for cover if the application is accepted. Digitalisation in healthcare and wide availability of EHR will make the use of cognitive systems more effective to this end.
Predictive analytics can streamline and cut the costs of the underwriting
Predictive modelling – the use of advanced statistical techniques and data analysis to make inferences or identify meaningful relationships in order to predict future outcomes – can be an alternative means to differentiate and select risks.
It would appear that the life industry has been relatively slow in implementing advanced data mining and predictive analytics techniques. But in the future, easier access to data in digital form and from non-traditional sources will enable a more widespread use of predictive analytics in insurance underwriting. Ability to retrieve medical records instantaneously will accelerate the process and enable better underwriting decisions.
Better understanding of risks will push the boundaries of insurability
Technological advancements and a flood of data will lead to better understanding of risks and a shift from traditional, experience-based underwriting to a real-time, exposure-based approach.
Life insurers traditionally have assessed health risks and asked questions about lifestyle behaviours linked to a higher risk of mortality just once – at the point of selling a policy. Technology now allows insurers under certain circumstances to access data regularly with the permission of high-risk customers in exchange for insurance that had at one point been unaffordable or unavailable. Now insurance protection can be offered as a renewable cover where the insured is requested to provide continuous health data.
Conversely, low-risk customers may want to push data to their insurance providers in exchange for lower premium rates.
Digital technology allows for integration of underwriting and sales processes
By taking advantage of digital technology, advisers can combine the advantages of automation and personalised advice.
The life insurance industry has put significant effort into automating the underwriting process. Other applications have also been developed that allow agents to enter client risk parameters into a single system, get a binding quote from several insurers and conclude the transaction immediately. This removes the need for advisers to base their assessment on preliminary quotes, deal with several different entry forms and get binding quotes from different insurers.
Streamlining data collection and accelerating the underwriting process helps avoid interruptions and is likely to increase the number of transactions that reach the closing stage.
Conclusion
The life insurance sector is set for fundamental transformation, brought about by technological advancements and new digital analytic techniques. The impact is expected to span the entire insurance value chain from product development and underwriting through to distribution, services and claims.
This presents an unprecedented opportunity to significantly transform the way underwriters work and to expand the ways in which underwriting can contribute value to the business. Insurance companies will need to develop new skill sets and also may require external advice to maximise their opportunities in this “new world”.
Ms Teresa To is Director and Head Underwriting Solutions and Initiatives of Swiss Re’s Life & Health Products in Asia.